pytorch and poetry

[yonimedhub]

• [X ] I am on the latest Poetry version.
• [X ] I have searched the issues of this repo and believe that this is not a duplicate.
• [ X] If an exception occurs when executing a command, I executed it again in debug mode (-vvv option).

• OS version and name: docker on linux with cuda
• Poetry version: 1.1.7
• Link of a Gist with the contents of your pyproject.toml file: <below

Issue

pytorch is now pep503 compliant (pytorch/pytorch#25639 (comment)) but I still can't add torch and torchvision.
I've tried using this:

torch = {url = "https://download.pytorch.org/whl/cu111/torch-1.9.0%2Bcu111-cp38-cp38-linux_x86_64.whl"}
torchvision = {url = "https://download.pytorch.org/whl/cu111/torchvision-0.10.0%2Bcu111-cp38-cp38-linux_x86_64.whl"}

but got this:

Updating dependencies
Resolving dependencies... (326.0s)

  SolverProblemError

  Because torchvision (0.10.0+cu111) depends on torch (1.9.0)
   and  depends on torch (1.9.0+cu111), torchvision is forbidden.
  So, because  depends on torchvision (0.10.0+cu111), version solving failed.

and if I try using

 [[tool.poetry.source]]
 name = "torch_rep"
 url = "https://download.pytorch.org/whl/cu111/"

I end up disabling the pypi and can't download anything else...

[Piroro-hs]

[tool.poetry.dependencies]
torch = { version = "=1.9.0+cu111", source = "pytorch" }
torchvision = { version = "=0.10.0+cu111", source = "pytorch" }

[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu111/"
secondary = true

This works for me with poetry v1.2.0a1 (poetry install is so slow due to #4035, though).

[yonimedhub]

this is my pyproject.toml:

[tool.poetry]
name = ""
version = "0.1.0"
description = ""
authors = ["Your Name <you@example.com>"]

[tool.poetry.dependencies]
python = "3.8.5"
numpy = "^1.21.0"
matplotlib = "^3.4.2"
torch = { version = "=1.9.0+cu111", source = "pytorch" }
torchvision = { version = "=0.10.0+cu111", source = "pytorch" }

[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu111/"
secondary = true

[tool.poetry.dev-dependencies]


[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

with this I get the following error (with poetry update):

  SolverProblemError

  Because torchvision (0.10.0+cu111) depends on torch (1.9.0)
   and  depends on torch (=1.9.0+cu111), torchvision is forbidden.
  So, because  depends on torchvision (=0.10.0+cu111), version solving failed.

and if I put both torch and torchvision in comments I still get the following error:

  RepositoryError

  403 Client Error: Forbidden for url: https://download.pytorch.org/whl/cu111/matplotlib/

[renan-r-santos]

This is a duplicate of #2543, #3855, #3306 and some others.

An ugly workaround while this is not fixed is:

[tool.poetry]
name = "test"
version = "0.1.0"
description = ""
authors = ["author <author@author.com>"]

[tool.poetry.dependencies]
python = "^3.9"

[tool.poe.tasks]
## PyTorch with CUDA 11.1. If PyTorch is imported first, importing Tensorflow will detect CUDA + cuDNN bundled with PyTorch
## Run with the command "poe force-cuda11"
## See https://github.com/python-poetry/poetry/issues/2543
force-cuda11 = "pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html"

[tool.poetry.dev-dependencies]
black = "^21.6b0"
poethepoet = "^0.10.0"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

[wangm23456]

@scherzocrk when I "poetry install", the torch will be " • Updating torch (1.9.0+cu111 -> 1.9.0)"

[kikohs]

@wangm23456 I wrote the hack above using the task-runner, I have the same issue, poetry always rolls back to regular torch. It gets me crazy..

[bennylut]

[tool.poetry.dependencies]
torch = { version = "=1.9.0+cu111", source = "pytorch" }
torchvision = { version = "=0.10.0+cu111", source = "pytorch" }

[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cu111/"
secondary = true

This works for me with poetry v1.2.0a1 (poetry install is so slow due to #4035, though).

Just tested it on v1.2.0a2 and with the secondary source poetry install is still very slow and takes more than 18m in my machine.

[david-waterworth]

Also worse than not being able to install torchvision, for the same reason you cannot install any ML package which depends on torch. For example allennlp requires torch>=1.6.0,<1.11.0 and torchvision>=0.8.1,<0.12.0, so the result is:

  Because no versions of allennlp match >2.8.0,<3.0.0
   and allennlp (2.8.0) depends on torchvision (>=0.8.1,<0.12.0), allennlp (>=2.8.0,<3.0.0) requires torchvision (>=0.8.1,<0.12.0).
  Thus, allennlp (>=2.8.0,<3.0.0) requires torch (1.7.0 || 1.7.1 || 1.8.0 || 1.8.1 || 1.9.0 || 1.9.1 || 1.10.0 || 1.10.0+cu102).
  So, because disambiguation depends on both torch (1.9.1+cu111) and allennlp (^2.8.0), version solving failed.

[psinger]

Coming back half a year later it seems there is still no solution for Pytorch related packages in poetry. Will this ever get solved? Otherwise need to move to other solutions.

[mhnatiuk]

yeah, about that. I want to add this functionality, so my team can use poetry. Otherwise, we'll stick to conda, which is not optimal, bc poetry plays so well with CI & CD pipelines, espescially for packaging software

[psinger]

Yeah, we unfortunately had to decide now to move away from poetry to good old pipenv. Let's hope there will be a solution as some point for poetry.

[Kavan72]

@psinger here is the good news for you. I just tested with poetry version 1.2.0b1 for cpu and cuda version of pytorch and both are working fine for me. Here is the my dependencies

[tool.poetry.dependencies]
python = "^3.8"
fastapi = "^0.75.0"
gunicorn = "^20.1.0"
loguru = "^0.6.0"
torch = {url = "https://download.pytorch.org/whl/cpu/torch-1.11.0%2Bcpu-cp38-cp38-linux_x86_64.whl"}
torchaudio = {url = "https://download.pytorch.org/whl/cpu/torchaudio-0.11.0%2Bcpu-cp38-cp38-linux_x86_64.whl"}
torchvision = {url = "https://download.pytorch.org/whl/cpu/torchvision-0.12.0%2Bcpu-cp38-cp38-linux_x86_64.whl"}
transformers = "^4.17.0"

[dan-ringwald]

@Kavan72 thx for the update, it works.

However the dependencies resolving time is absolutely huge (600+ seconds), would it be possible to ignore dependency solving for a package, or hard code the dependencies so that poetry doesn't have to full scan a 1.5Go package ?

[kikohs]

The solution of @Kavan72 is cool but unfortunately still a work around as you need to target a specific python version, and OS which goes against the whole idea of Poetry.
My ugly workaround (with pip install) is not better either...

[dan-ringwald]

@kikohs I ended up using poe the poet as a task runner too in the end. To avoid rolling back to cpu torch, you have to install every torch-using lib with pip through the task-runner too (e.g. transformers)

[suneeta-mall]

This issue is blocking "unlocking the poetry" potentials when it comes to leveraging poetry in the stack that uses PyTorch and its ecosystem.

I have tried with following:
toml setting

[[tool.poetry.source]]
name = "torch"
url = "https://download.pytorch.org/whl/cu113"
secondary = true
default = false

but the default false is not recognized for some reason and ends up getting the 403:
Error:

403 Client Error: Forbidden for url: https://download.pytorch.org/whl/cpu/mypy

As mentioned in this ticket, #4704 this is a known issue. However, amongst all possible ways to address this issue, this solution of using secondary sources seems to be the ideal fix for the issue in question.

As a short-term interim, I have also tried platform and version-specific settings. This would work fine if PyTorch was my leaf dependency. Because my setup involves using PyTorch, Torchvision, and Pytorch lightening. Because more dependencies rely on PyTorch, just specifying torch wheels in toml fails to solve the dependency:
toml setting

torch = [
    { url="https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp37-cp37m-linux_x86_64.whl", python=">=3.7,<3.8", markers="sys_platform == 'linux'"},
    { url="https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp38-cp38-linux_x86_64.whl", python=">=3.8,<3.9", markers="sys_platform == 'linux'"},
    { url="https://download.pytorch.org/whl/cu113/torch-1.11.0%2Bcu113-cp39-cp39-linux_x86_64.whl", python=">=3.9,<3.10", markers="sys_platform == 'linux'"},
    { version = "=1.11.0", markers = "sys_platform == 'darwin' or sys_platform == 'win32'" },
]
torchvision = [
    { url="https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp37-cp37m-linux_x86_64.whl", python=">=3.7,<3.8", markers="sys_platform == 'linux'"},
    { url="https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp38-cp38-linux_x86_64.whl", python=">=3.8,<3.9", markers="sys_platform == 'linux'"},
    { url="https://download.pytorch.org/whl/cu113/torchvision-0.12.0%2Bcu113-cp39-cp39-linux_x86_64.whl", python=">=3.9,<3.10", markers="sys_platform == 'linux'"},
    { version = "=0.12.0", markers = "sys_platform == 'darwin' or sys_platform == 'win32'" },
]

Error:

  SolverProblemError

  Because torchvision (0.12.0+cu113) depends on torch (1.11.0)
   and XXXX-app depends on torch (1.11.0+cu113), torchvision is forbidden.
  So, because XXXX-app depends on torchvision (0.12.0+cu113), version solving failed.

I have been in knots with this one, particularly because there are so many issues open around this issue:
#2543
#4231
#3855
#2613
#4704
#2339

The only solution that works cross-platform is https://github.com/nat-n/poethepoet but that is not a great solution either (not lining up with lock file, not using same cache etc, the need for additional pip run!). It would be great if we can fix this issue.

(this is a duplicate comment from #4704 (comment), posting again as this is very relevant ticket again)

[abn]

Thanks for the detailed post @suneeta-mall. I want to try and break out targetted improvements to help with the pytorch use case being better supported. Let me try and addres a few things in your post with that intent.

but the default false is not recognized for some reason and ends up getting the 403

This is expected in that default = false is the default for legacy sources. The way the default setting works is more like "disable PyPI", ie. if set to true the project disables PyPI and makes it the "fallback" among all leacy sources (govered by secondary = true in your case). Personally, I think this need to reworked to make it clearer in Poetry.

Also see #4704 (comment).

Regarding the local build tag resolving issue, the I suspect this is the same as being taked about in #4729 (comment). That, in theory atleast, should fix this issue.

From the Poetry side once the local tag solving is fixed, I suspect things will improve. If that is not the case please do let me know.

For those working with the PyTorch community, if you can work with the PyTorch team to get the following added/fixed, your experience when using PyTorch with Poetry might be improved.

1. The lack of file hashes being exposed in the repository means that Poetry has to download each artifact unnecessarily. As per PEP 503. this can be appended to the file URL as #<hashname>=<hashvalue>. This can be done easily by the index admins I beleive.
2. While fixing (1) will reduce the number of required downloads, files still will be downloaded again when there is a need to inspect metadata (ie. Requires-Dist etc. from the wheel). This can be avoided too if PEP 658 gets implemented. I suspect this will need to also be supported by Poetry as well.
3. Would be great if requesting a non-existant resource returns a 404 instead of 403. If I understand correct, this is because they have not set s3:ListBucket permission for public users (assuming they use S3 for this).

PS: One could in theory generate and host files required for 1 and 2 using a CI/CD job + vercel/fastly etc with some packaging code if so motivated. The links can still retain the upstream file link, but with sha256 appended and a new .metadata file generated and served.

[chanansh]

I am using mac for local development and a docker for publishing. The following allowed me to use a single pyproject file for both, hope it will help someone:

torch = [{markers = "sys_platform == 'macos'", url = "https://download.pytorch.org/whl/cpu/torch-1.13.0-cp310-none-macosx_11_0_arm64.whl"},
{markers = "sys_platform == 'linux'", url="https://download.pytorch.org/whl/torch-1.13.0-cp310-cp310-manylinux2014_aarch64.whl"}]

python version 3.10.6

[mywristbands]

Thanks @chanansh for that answer. However, to make it succssfully install on my mac with an M1 Pro chip, I had to change the "sys_platform == 'macos'" to "sys_platform == 'darwin'".

Also, note that it would fail to install on both the docker container and mac if I used python version 3.11.0. But using python version 3.10.9 worked.

[hongbo-miao]

darwin is more accurate I think. I posted a summary at https://stackoverflow.com/a/74794784/2000548
Wish Poetry can be smarter for PyTorch in future so that we do not need manually set them 😃

Here is a copy:

---

[tool.poetry.dependencies]
python = "3.10.x"
torch = [
  {markers = "sys_platform == 'darwin' and platform_machine == 'arm64'", url = "https://files.pythonhosted.org/packages/79/b3/eaea3fc35d0466b9dae1e3f9db08467939347b3aaa53c0fd81953032db33/torch-1.13.0-cp310-none-macosx_11_0_arm64.whl"},
  {markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'", url = "https://files.pythonhosted.org/packages/b6/79/ead6840368f294497591af143980372ff956fc4c982c457a8b5610a5a1f3/torch-1.13.0-cp310-none-macosx_10_9_x86_64.whl"},
  {markers = "sys_platform == 'linux'", url="https://files.pythonhosted.org/packages/5c/61/b0303b8810c1300e75e8e665d043f6c2b272a4da60e9cc33416cde8edb76/torch-1.13.0-cp310-cp310-manylinux2014_aarch64.whl"}
]

• arm64 is for macOS with M1/M2 chip. x86_64 is for macOS with Intel chip.
• For Linux, you can also define platform_machine if you are using multiple architectures (In the demo above, it is using aarch64 wheel).

You can find all wheel URLs at either one of

• https://download.pytorch.org/whl/torch/
• https://pypi.org/project/torch/#files

You can find your current platform and architecture by using Python command:

> python
>>> import sys
>>> sys.platform
'darwin'
>>> import platform
>>> platform.machine()
'arm64'

You can find the list of sys.platform at here.

[hongbo-miao]

Add an update:

After upgrading to latest Poetry 1.3.1, I can simply use this now:

[tool.poetry.dependencies]
python = "3.10.x"
torch = "1.13.1"

It succeed both on both my MacBook Pro with M1 chip and Linux (Ubuntu) in the pipeline. (I did regenerate poetry.lock file to make sure it actually works)

Here is the pyproject.toml.

For Linux (Ubuntu) in the pipeline part, you can see this which shows succeed:

• GitHub Actions code using Ubuntu
• GitHub Actions successfully running log

[mywristbands]

@hongbo-miao I tried upgrading to Poetry 1.3.1, editing my pyproject.toml file as described, and then running poetry update torch. It works fine on my M1 Mac, but when I try installing inside a Linux docker container running on this same M1 Mac, I get a bunch of errors like:

RuntimeError
#11 1.629 
#11 1.629   Unable to find installation candidates for nvidia-cublas-cu11 (11.10.3.66)
#11 1.629 
#11 1.629   at /usr/local/lib/python3.10/site-packages/poetry/installation/chooser.py:105 in choose_for
#11 1.637       101│ 
#11 1.637       102│             links.append(link)
#11 1.637       103│ 
#11 1.637       104│         if not links:
#11 1.638     → 105│             raise RuntimeError(f"Unable to find installation candidates for {package}")
#11 1.638       106│ 
#11 1.639       107│         # Get the best link
#11 1.640       108│         chosen = max(links, key=lambda link: self._sort_key(package, link))
#11 1.640       109│ 

[lhoestq]

With poetry==1.3.2 on python 3.9.15 in docker python:3.9.15-slim on a mac m2 I'm getting

ERROR: torch-1.13.1+cpu-cp39-cp39-linux_x86_64.whl is not a supported wheel on this platform.

Updating to poetry==1.4.0 fixed it !

EDIT:

and I ended up using

torch = [
    { url = "https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl", markers = "sys_platform == 'linux' and platform_machine != 'aarch64'"},
    { url = "https://download.pytorch.org/whl/cpu/torch-1.13.1%2Bcpu-cp39-cp39-linux_x86_64.whl", markers = "sys_platform == 'darwin' and platform_machine != 'arm64'"},
    { url = "https://download.pytorch.org/whl/cpu/torch-1.13.1-cp39-none-macosx_11_0_arm64.whl", markers = "sys_platform == 'darwin' and platform_machine == 'arm64'"},
    { url = "https://download.pytorch.org/whl/torch-1.13.1-cp39-cp39-manylinux2014_aarch64.whl", markers = "sys_platform == 'linux' and platform_machine == 'aarch64'"},
]

[rjurney]

I still have this issue.

[thornewolf]

confirming issue still exists

[cateseale]

The below resolves in a few seconds with poetry 1.5.1, but unable to work with torch due to ValueError: libcublas.so.*[0-9] not found in the system path, also referenced in this issue here.

[tool.poetry]
name = "pytorch-poetry-test"
version = "0.1.0"
description = ""
authors = ["cate <catherineseale@gmail.com>"]
readme = "README.md"
packages = [{include = "pytorch_poetry_test"}]

[tool.poetry.dependencies]
python = "^3.9"
torch = "^2.0.1"
torchvision = "^0.15.2"


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

If I remove torchvision and downgrade to torch 2.0.0, this is successful and can use torch ok, however torchvision then can't resolve:

name = "pytorch-poetry-test"
version = "0.1.0"
description = ""
authors = ["cate <catherineseale@gmail.com>"]
readme = "README.md"
packages = [{include = "pytorch_poetry_test"}]

[tool.poetry.dependencies]
python = "^3.9"
torch = ">=2.0.0, !=2.0.1"


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

Because no versions of torchvision match >0.15.2,<0.16.0
 and torchvision (0.15.2) depends on torch (2.0.1), torchvision (>=0.15.2,<0.16.0) requires torch (2.0.1).
So, because pytorch-poetry-test depends on both torch (>=2.0.0, !=2.0.1) and torchvision (^0.15.2), version solving failed.

Updating from torch==2.0.0 to add both torch and torchvision in again removes a lot of cuda stuff, and setuptools?

  • Removing cmake (3.27.2)
  • Removing lit (16.0.6)
  • Removing nvidia-cublas-cu11 (11.10.3.66)
  • Removing nvidia-cuda-cupti-cu11 (11.7.101)
  • Removing nvidia-cuda-nvrtc-cu11 (11.7.99)
  • Removing nvidia-cuda-runtime-cu11 (11.7.99)
  • Removing nvidia-cudnn-cu11 (8.5.0.96)
  • Removing nvidia-cufft-cu11 (10.9.0.58)
  • Removing nvidia-curand-cu11 (10.2.10.91)
  • Removing nvidia-cusolver-cu11 (11.4.0.1)
  • Removing nvidia-cusparse-cu11 (11.7.4.91)
  • Removing nvidia-nccl-cu11 (2.14.3)
  • Removing nvidia-nvtx-cu11 (11.7.91)
  • Removing setuptools (68.1.0)
  • Removing triton (2.0.0)
  • Removing wheel (0.41.1)

[TCherici]

name = "pytorch-poetry-test"

Not sure if it is relevant, but I also had issues with setuptools being removed in my anaconda+poetry environment. Updating anaconda with conda update conda solved my issue.

[hongbo-miao]

I think @cateseale is correct. For people failed to install PyTorch by Poetry after May 9, 2023.
This is because of this PyTorch issue at pytorch/pytorch#100974

So instead of using

torch = "2.0.1"
torchvision = "0.15.2"

This should work:

torch = "2.0.0"
torchvision = "0.15.1"

Hopefully next PyTorch version will fix this issue. 😃

[rjurney]

Is this getting a fix?

[jphme]

Just commenting to confirm that its still a problem.

I use a Docker container with a Torch/Cuda env that shouldnt be touched and poetry config virtualenvs.create false for deployment.

If I specify torch = { version = "=2.0.1+cu118", source = "cuda" } with the correct source, poetry will re-download Torch for Python 3.10 (even if Python 3.9. is explicitly specified in the toml and there is no Python 3.10 installed) and if I add torch = { url = "https://download.pytorch.org/whl/cu118/torch-2.0.1%2Bcu118-cp39-cp39-linux_x86_64.whl"}, Poetry will also redownload Torch instead of using the currently installed vesion.

Is there any solution to use the pre-isntalled version? (I need to fix it in the pyproject.toml file to prevent other libraries from overwriting it - with pip this works fine....

[rjurney]

I'm confused how this remains an issue... shouldn't the newer version just take care of this?

https://x.com/rjurney/status/1717533202527756797?s=20

[david-waterworth]

Yes it's broken again for me torch = {version = ">=2.0.0, !=2.0.1"} worked fine for me (I specifically excluded 2.0.1 because that version was accidentally shipped without the nvidia dependencies, and I specify torch as an explicit dependency before any other packages that themselves require torch such as transformers) until today.

Its started failing again with the release of torch 2.1 - poetry and pip install different things again

pip install torch installs torch 2.1.0 plus all the various nvidia libs (which install the required cuda version)

but poetry install on a clean machine only installed torch 2.1.0 - it should have also installed

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,
nvidia-cusparse-cu12,
nvidia-cudnn-cu12
nvidia-cublas-cu12-12.1.3.1
nvidia-cuda-cupti-cu12-12.1.105
nvidia-cuda-nvrtc-cu12-12.1.105
nvidia-cuda-runtime-cu12-12.1.105
nvidia-cudnn-cu12-8.9.2.26
nvidia-cufft-cu12-11.0.2.54
nvidia-curand-cu12-10.3.2.106
nvidia-cusolver-cu12-11.4.5.107
nvidia-cusparse-cu12-12.1.0.106
nvidia-nccl-cu12-2.18.1
nvidia-nvjitlink-cu12-12.3.52
nvidia-nvtx-cu12-12.1.105
triton-2.1.0

So import torch then fails with missing .so

It works fine when I specifically target torch==2.0.0 - I might raise a separate issue

[rjurney]

@david-waterworth I am confused.

I specifically excluded 2.0.1 because that version was accidentally shipped without the nvidia dependencies, and I specify torch as an explicit dependency before any other packages that themselves require torch such as transformers) until today.

If the current version is 2.1.0 - isn't that what it is likely to try to use - thus avoiding the 2.0.1 problem? Or does it still happen?

[david-waterworth]

Sorry I wasn't clearm yes it's trying to use 2.1.0 which is also broken. There's an issue with the torch packaging, which results in the metadata for dependencies not being correctly uploaded to PyPi (as I understand it). Both 2.0.1 and 2.1.0 are affected so cannot be installed with poetry.

setting

torch = {version = ">=2.0.0, !=2.0.1, !=2.1.0"}

prevents the poetry failure, and 2.1.1 is apparently being prepared to fix the problem on the torch side.

[naarkhoo]

any better solution than #4231 (comment)

[github-actions]

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