`poetry` adding latest version of a package in `lock` file even though different version is specified in `pyproject.toml`

[nsabir2011]

• Poetry version: 1.7.1<!-- Replace with version, e.g. from poetry --version -->
• Python version: 3.8.18
• OS version and name: WSL Ubuntu 22.04.3
• pyproject.toml: https://gist.github.com/nsabir2011/ee8b2a8acc6837c9ca21af0481009c78 Also attaching it here as a Code block since it is quite small

• [x] I am on the latest stable Poetry version, installed using a recommended method.
• [x] I have searched the issues of this repo and believe that this is not a duplicate.
• [x] I have consulted the FAQ and blog for any relevant entries or release notes.
• [ ] If an exception occurs when executing a command, I executed it again in debug mode (-vvv option) and have included the output below.

Issue

I wanted to add every possible versions of torch for my needs in the .toml file. Basically I want to install cuda capable version when the platform architecture is x86_64 and install the non-cuda version when platform is aarch64 or when the OS is macos. Here's my pyproject.toml file:

[tool.poetry]
name = "my package"
version = "1.6.0"
description = "do stuff"
authors = ["Nazmus Sakib Abir"]
license = ""
readme = "README.md"

[tool.poetry.dependencies]
python = "~3.8"
albumentations = ">=1.1.0"
jsonargparse = { version = ">=4.3", extras = ["signatures"] }
kornia = "0.6.8"
matplotlib = ">=3.4.3"
omegaconf = ">=2.1.1"
opencv-python = ">=4.5.3.56"
pandas = ">=1.1.0"
timm = "0.5.4"
torch = [
    { version = "1.12.1+cu113", source = "pytorch-cuda", platform = "linux", markers="platform_machine=='x86_64'" },
    { version = "1.12.1+cu113", source = "pytorch-cuda", platform = "win32", markers="platform_machine=='x86_64'" },
    { version = "1.12.1", source = "PyPI", platform = "linux", markers="platform_machine=='aarch64'" },
    { version = "1.12.1", source = "PyPI", platform = "darwin" },
]
torchmetrics = "0.10.2"
torchvision = [
    { version = "0.13.1+cu113", source = "pytorch-cuda", platform = "linux", markers="platform_machine=='x86_64'" },
    { version = "0.13.1+cu113", source = "pytorch-cuda", platform = "win32", markers="platform_machine=='x86_64'" },
    { version = "0.13.1", source = "PyPI", platform = "linux", markers="platform_machine=='aarch64'" },
    { version = "0.13.1", source = "PyPI", platform = "darwin" },
]
pytorch-lightning = ">=1.6.0,<=1.7.0"
click = "8.1.3"
pillow = "^9.5.0"

[tool.poetry.group.api.dependencies]
SQLAlchemy = "1.4.*"
gunicorn = "20.1.0"
jsonschema = "4.17.*"
Flask = "2.2.2"
Flask-Cors = "3.0.10"
Flask-RESTful = "0.3.9"
requests = "2.28.*"
torchserve = "0.7.0"
torch-model-archiver = "0.7.0"
nvgpu = "0.9.0"
pynvml = "11.4.1"
werkzeug = "^2.3.8"

[tool.poetry.group.dev.dependencies]
black = "22.6.0"
isort = "5.10.1"
pylint = "2.14.5"
flake8 = "4.0.1"
pre-commit = ">=2.15.0"
pyarmor = "7.7.4"
tox = ">=3.24.3"
xlsxwriter = "3.0.3"

[tool.poetry.group.docs]
optional = true

[tool.poetry.group.docs.dependencies]
furo = "2021.7.31b41"
myst-parser = "^0.18.0"
Sphinx = ">=4.1.2"
sphinx-autoapi = "^1.9.0"
sphinxemoji = "0.1.8"
mkdocs = "1.4.3"
mkdocs-material = "9.1.15"
mkdocs-static-i18n = "0.56"
pymdown-extensions = "10.0.1"

[tool.poetry.group.test.dependencies]
pytest = "^7.1.3"
pytest-cov = "^4.0.0"
torchserve = "0.7.0"

[[tool.poetry.source]]
name = "pytorch-cuda"
url = "https://download.pytorch.org/whl/cu113"
priority = "explicit"

[build-system]
requires = ["poetry-core"]
build-backend = "poetry.core.masonry.api"

[tool.poetry.plugins] # Optional super table

[tool.poetry.plugins."console_scripts"]
"hamacho" = "hamacho.main:cli"

I noticed that after I do poetry lock it adds dependencies required by the latest version of torch (currently 2.2.1) in the lock file: I am including only the related sections.

...
[[package]]
name = "mpmath"
version = "1.3.0"
description = "Python library for arbitrary-precision floating-point arithmetic"
optional = false
python-versions = "*"
files = [
    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
]

[package.extras]
develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
docs = ["sphinx"]
gmpy = ["gmpy2 (>=2.1.0a4)"]
tests = ["pytest (>=4.6)"]
...
[[package]]
name = "nvidia-cublas-cu12"
version = "12.1.3.1"
description = "CUBLAS native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl", hash = "sha256:ee53ccca76a6fc08fb9701aa95b6ceb242cdaab118c3bb152af4e579af792728"},
    {file = "nvidia_cublas_cu12-12.1.3.1-py3-none-win_amd64.whl", hash = "sha256:2b964d60e8cf11b5e1073d179d85fa340c120e99b3067558f3cf98dd69d02906"},
]

[[package]]
name = "nvidia-cuda-cupti-cu12"
version = "12.1.105"
description = "CUDA profiling tools runtime libs."
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:e54fde3983165c624cb79254ae9818a456eb6e87a7fd4d56a2352c24ee542d7e"},
    {file = "nvidia_cuda_cupti_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:bea8236d13a0ac7190bd2919c3e8e6ce1e402104276e6f9694479e48bb0eb2a4"},
]

[[package]]
name = "nvidia-cuda-nvrtc-cu12"
version = "12.1.105"
description = "NVRTC native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:339b385f50c309763ca65456ec75e17bbefcbbf2893f462cb8b90584cd27a1c2"},
    {file = "nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:0a98a522d9ff138b96c010a65e145dc1b4850e9ecb75a0172371793752fd46ed"},
]

[[package]]
name = "nvidia-cuda-runtime-cu12"
version = "12.1.105"
description = "CUDA Runtime native Libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:6e258468ddf5796e25f1dc591a31029fa317d97a0a94ed93468fc86301d61e40"},
    {file = "nvidia_cuda_runtime_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:dfb46ef84d73fababab44cf03e3b83f80700d27ca300e537f85f636fac474344"},
]

[[package]]
name = "nvidia-cudnn-cu12"
version = "8.9.2.26"
description = "cuDNN runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl", hash = "sha256:5ccb288774fdfb07a7e7025ffec286971c06d8d7b4fb162525334616d7629ff9"},
]

[package.dependencies]
nvidia-cublas-cu12 = "*"

[[package]]
name = "nvidia-cufft-cu12"
version = "11.0.2.54"
description = "CUFFT native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
]

[[package]]
name = "nvidia-curand-cu12"
version = "10.3.2.106"
description = "CURAND native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
]

[[package]]
name = "nvidia-cusolver-cu12"
version = "11.4.5.107"
description = "CUDA solver native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
]

[package.dependencies]
nvidia-cublas-cu12 = "*"
nvidia-cusparse-cu12 = "*"
nvidia-nvjitlink-cu12 = "*"

[[package]]
name = "nvidia-cusparse-cu12"
version = "12.1.0.106"
description = "CUSPARSE native runtime libraries"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
]

[package.dependencies]
nvidia-nvjitlink-cu12 = "*"

[[package]]
name = "nvidia-nccl-cu12"
version = "2.19.3"
description = "NVIDIA Collective Communication Library (NCCL) Runtime"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d"},
]

[[package]]
name = "nvidia-nvjitlink-cu12"
version = "12.3.101"
description = "Nvidia JIT LTO Library"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-manylinux1_x86_64.whl", hash = "sha256:64335a8088e2b9d196ae8665430bc6a2b7e6ef2eb877a9c735c804bd4ff6467c"},
    {file = "nvidia_nvjitlink_cu12-12.3.101-py3-none-win_amd64.whl", hash = "sha256:1b2e317e437433753530792f13eece58f0aec21a2b05903be7bffe58a606cbd1"},
]

[[package]]
name = "nvidia-nvtx-cu12"
version = "12.1.105"
description = "NVIDIA Tools Extension"
optional = false
python-versions = ">=3"
files = [
    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
]
...
[[package]]
name = "sympy"
version = "1.12"
description = "Computer algebra system (CAS) in Python"
optional = false
python-versions = ">=3.8"
files = [
    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
]

[package.dependencies]
mpmath = ">=0.19"
...
[[package]]
name = "torch"
version = "1.12.1"
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
optional = false
python-versions = ">=3.7.0"
files = [
    {file = "torch-1.12.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:9c038662db894a23e49e385df13d47b2a777ffd56d9bcd5b832593fab0a7e286"},
    {file = "torch-1.12.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:4e1b9c14cf13fd2ab8d769529050629a0e68a6fc5cb8e84b4a3cc1dd8c4fe541"},
    {file = "torch-1.12.1-cp310-cp310-win_amd64.whl", hash = "sha256:e9c8f4a311ac29fc7e8e955cfb7733deb5dbe1bdaabf5d4af2765695824b7e0d"},
    {file = "torch-1.12.1-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:976c3f997cea38ee91a0dd3c3a42322785414748d1761ef926b789dfa97c6134"},
    {file = "torch-1.12.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:68104e4715a55c4bb29a85c6a8d57d820e0757da363be1ba680fa8cc5be17b52"},
    {file = "torch-1.12.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:743784ccea0dc8f2a3fe6a536bec8c4763bd82c1352f314937cb4008d4805de1"},
    {file = "torch-1.12.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:b5dbcca369800ce99ba7ae6dee3466607a66958afca3b740690d88168752abcf"},
    {file = "torch-1.12.1-cp37-cp37m-win_amd64.whl", hash = "sha256:f3b52a634e62821e747e872084ab32fbcb01b7fa7dbb7471b6218279f02a178a"},
    {file = "torch-1.12.1-cp37-none-macosx_10_9_x86_64.whl", hash = "sha256:8a34a2fbbaa07c921e1b203f59d3d6e00ed379f2b384445773bd14e328a5b6c8"},
    {file = "torch-1.12.1-cp37-none-macosx_11_0_arm64.whl", hash = "sha256:42f639501928caabb9d1d55ddd17f07cd694de146686c24489ab8c615c2871f2"},
    {file = "torch-1.12.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0b44601ec56f7dd44ad8afc00846051162ef9c26a8579dda0a02194327f2d55e"},
    {file = "torch-1.12.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:cd26d8c5640c3a28c526d41ccdca14cf1cbca0d0f2e14e8263a7ac17194ab1d2"},
    {file = "torch-1.12.1-cp38-cp38-win_amd64.whl", hash = "sha256:42e115dab26f60c29e298559dbec88444175528b729ae994ec4c65d56fe267dd"},
    {file = "torch-1.12.1-cp38-none-macosx_10_9_x86_64.whl", hash = "sha256:a8320ba9ad87e80ca5a6a016e46ada4d1ba0c54626e135d99b2129a4541c509d"},
    {file = "torch-1.12.1-cp38-none-macosx_11_0_arm64.whl", hash = "sha256:03e31c37711db2cd201e02de5826de875529e45a55631d317aadce2f1ed45aa8"},
    {file = "torch-1.12.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:9b356aea223772cd754edb4d9ecf2a025909b8615a7668ac7d5130f86e7ec421"},
    {file = "torch-1.12.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:6cf6f54b43c0c30335428195589bd00e764a6d27f3b9ba637aaa8c11aaf93073"},
    {file = "torch-1.12.1-cp39-cp39-win_amd64.whl", hash = "sha256:f00c721f489089dc6364a01fd84906348fe02243d0af737f944fddb36003400d"},
    {file = "torch-1.12.1-cp39-none-macosx_10_9_x86_64.whl", hash = "sha256:bfec2843daa654f04fda23ba823af03e7b6f7650a873cdb726752d0e3718dada"},
    {file = "torch-1.12.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:69fe2cae7c39ccadd65a123793d30e0db881f1c1927945519c5c17323131437e"},
]

[package.dependencies]
typing-extensions = "*"

[[package]]
name = "torch"
version = "1.12.1+cu113"
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
optional = false
python-versions = ">=3.7.0"
files = [
    {file = "torch-1.12.1+cu113-cp310-cp310-linux_x86_64.whl", hash = "sha256:be682ef94e37cd3f0768b8ce6106705410189df2c365d65d7bc1bebb302d84cd"},
    {file = "torch-1.12.1+cu113-cp310-cp310-win_amd64.whl", hash = "sha256:8b83783f6537b48b75b6ba5d62d7acfd94546689223bb0d3a7d39886148b8d17"},
    {file = "torch-1.12.1+cu113-cp37-cp37m-linux_x86_64.whl", hash = "sha256:d8747d7d8add62213adb2ccf0e143aa9364d1e16c7e4bcdf45e302bc84404368"},
    {file = "torch-1.12.1+cu113-cp37-cp37m-win_amd64.whl", hash = "sha256:b331dc5ab6aeb092e849addb2a0490623988033befc54b4ff3e1a8e88fbc6fd2"},
    {file = "torch-1.12.1+cu113-cp38-cp38-linux_x86_64.whl", hash = "sha256:4adf483ac2d047534a7d023f0022bd8694d87627068ad6dddf186cb3273bbfa2"},
    {file = "torch-1.12.1+cu113-cp38-cp38-win_amd64.whl", hash = "sha256:9852174c19d753b071393da6f45a2b3f68d94cfbf1ee85a85d1a1870da5aec48"},
    {file = "torch-1.12.1+cu113-cp39-cp39-linux_x86_64.whl", hash = "sha256:01854806973840bdf3fc0efc84811539cb37101afd6498ef24a7d7f1af3cb010"},
    {file = "torch-1.12.1+cu113-cp39-cp39-win_amd64.whl", hash = "sha256:b20d19c379c8fc71f04f49b35c867732f0dfa19ec046af218dc77458c05424f7"},
]

[package.dependencies]
typing-extensions = "*"

[package.source]
type = "legacy"
url = "https://download.pytorch.org/whl/cu113"
reference = "pytorch-cuda"

[[package]]
name = "torch"
version = "2.2.1"
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
optional = false
python-versions = ">=3.8.0"
files = [
    {file = "torch-2.2.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:8d3bad336dd2c93c6bcb3268e8e9876185bda50ebde325ef211fb565c7d15273"},
    {file = "torch-2.2.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:5297f13370fdaca05959134b26a06a7f232ae254bf2e11a50eddec62525c9006"},
    {file = "torch-2.2.1-cp310-cp310-win_amd64.whl", hash = "sha256:5f5dee8433798888ca1415055f5e3faf28a3bad660e4c29e1014acd3275ab11a"},
    {file = "torch-2.2.1-cp310-none-macosx_10_9_x86_64.whl", hash = "sha256:b6d78338acabf1fb2e88bf4559d837d30230cf9c3e4337261f4d83200df1fcbe"},
    {file = "torch-2.2.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:6ab3ea2e29d1aac962e905142bbe50943758f55292f1b4fdfb6f4792aae3323e"},
    {file = "torch-2.2.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:d86664ec85902967d902e78272e97d1aff1d331f7619d398d3ffab1c9b8e9157"},
    {file = "torch-2.2.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:d6227060f268894f92c61af0a44c0d8212e19cb98d05c20141c73312d923bc0a"},
    {file = "torch-2.2.1-cp311-cp311-win_amd64.whl", hash = "sha256:77e990af75fb1675490deb374d36e726f84732cd5677d16f19124934b2409ce9"},
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fsspec = "*"
jinja2 = "*"
networkx = "*"
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nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cudnn-cu12 = {version = "8.9.2.26", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
sympy = "*"
triton = {version = "2.2.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
typing-extensions = ">=4.8.0"

[package.extras]
opt-einsum = ["opt-einsum (>=3.3)"]
optree = ["optree (>=0.9.1)"]
...
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name = "torchvision"
version = "0.13.1"
description = "image and video datasets and models for torch deep learning"
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pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
requests = "*"
torch = "1.12.1"
typing-extensions = "*"

[package.extras]
scipy = ["scipy"]

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pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
requests = "*"
torch = "1.12.1"
typing-extensions = "*"

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scipy = ["scipy"]

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url = "https://download.pytorch.org/whl/cu113"
reference = "pytorch-cuda"

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[package.dependencies]
numpy = "*"
pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
torch = "2.2.1"

[package.extras]
scipy = ["scipy"]
...

This also means that the poetry lock command takes quite a long time as it downloads the latest torch packages. This is really annoying for someone who has a slow internet in a developing country. I also don't know if this will correctly install the required packages on a M-series Mac.

Please guide me if I am doing anything wrong.

[dimbleby]

the environments that you describe in your torch requirements are non-exhaustive. pytorch-lightning depends on torch.

so you have left room for poetry to choose a more recent torch, satisfying the pytorch-lightning requirement, in the environments for which you have not specified an older torch.

please close

[nsabir2011]

@dimbleby Thanks. Closing this as it seems I made a mistake. However, I don't entirely understand what you mean. Could you kindly elaborate? Perhaps a small example?

[dimbleby]

what version of torch should poetry install on windows, not x86-64? Why not the most recent?

[nsabir2011]

@dimbleby Oh I see. Thanks for pointing it out. So it's better to do something like below, right?

...
torch = [
    { version = "1.12.1+cu113", source = "pytorch-cuda", platform = "linux", markers="platform_machine=='x86_64'" },
    { version = "1.12.1+cu113", source = "pytorch-cuda", platform = "win32", markers="platform_machine=='x86_64'" },
    { version = "1.12.1" },
]
torchvision = [
    { version = "0.13.1+cu113", source = "pytorch-cuda", platform = "linux", markers="platform_machine=='x86_64'" },
    { version = "0.13.1+cu113", source = "pytorch-cuda", platform = "win32", markers="platform_machine=='x86_64'" },
    { version = "0.13.1" },
]
...

[github-actions[bot]]

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