"ValueError: You selected an invalid strategy name" When DDPStrategy(process_group_backend="gloo") is passed

[11philip22]

Bug description

When I run this code on Python 3.12.8 with pytorch-lightning 2.4.0 I get a ValueError

What version are you seeing the problem on?

v2.4

How to reproduce the bug

ddp_gloo = DDPStrategy(process_group_backend="gloo")

trainer = Trainer(
    devices=2,
    # devices=1,
    accelerator='gpu',
    strategy=ddp_gloo,
    benchmark=True,
    logger=logger,
    callbacks=[checkpoint_callback, lr_monitor],
    check_val_every_n_epoch=1,
    max_epochs=30,
    # max_epochs=3,
)
trainer.fit(model, data_module)

Error messages and logs

Traceback (most recent call last):
  File "C:\Users\Philip\source\repos\insightface_alignment_lightning\src\train.py", line 59, in <module>
    main()
  File "C:\Users\Philip\source\repos\insightface_alignment_lightning\src\train.py", line 43, in main
    trainer = Trainer(
              ^^^^^^^^
  File "C:\Users\Philip\.conda\envs\lightning\Lib\site-packages\pytorch_lightning\utilities\argparse.py", line 70, in insert_env_defaults
    return fn(self, **kwargs)
           ^^^^^^^^^^^^^^^^^^
  File "C:\Users\Philip\.conda\envs\lightning\Lib\site-packages\pytorch_lightning\trainer\trainer.py", line 395, in __init__
    self._accelerator_connector = _AcceleratorConnector(
                                  ^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\Philip\.conda\envs\lightning\Lib\site-packages\pytorch_lightning\trainer\connectors\accelerator_connector.py", line 130, in __init__
    self._check_config_and_set_final_flags(
  File "C:\Users\Philip\.conda\envs\lightning\Lib\site-packages\pytorch_lightning\trainer\connectors\accelerator_connector.py", line 193, in _check_config_and_set_final_flags
    raise ValueError(
ValueError: You selected an invalid strategy name: `strategy=<lightning.pytorch.strategies.ddp.DDPStrategy object at 0x0000023622FA2240>`. It must be either a string or an instance of `pytorch_lightning.strategies.Strategy`. Example choices: auto, ddp, ddp_spawn, deepspeed, ... Find a complete list of options in our documentation at https://lightning.ai

Environment

Current environment

• CUDA:
  - GPU:
  - Quadro P6000
  - Quadro P6000
  - available: True
  - version: 12.4
• Lightning:
  - efficientnet-pytorch: 0.7.1
  - lightning: 2.4.0
  - lightning-utilities: 0.11.9
  - pytorch-lightning: 2.4.0
  - segmentation-models-pytorch: 0.3.5.dev0
  - torch: 2.5.1
  - torchmetrics: 1.6.0
  - torchvision: 0.20.1
• Packages:
  - absl-py: 2.1.0
  - aiohappyeyeballs: 2.4.4
  - aiohttp: 3.11.11
  - aiosignal: 1.3.2
  - albucore: 0.0.21
  - albumentations: 1.4.23
  - annotated-types: 0.7.0
  - attrs: 24.3.0
  - autocommand: 2.2.2
  - backports.tarfile: 1.2.0
  - brotli: 1.1.0
  - certifi: 2024.12.14
  - cffi: 1.17.1
  - charset-normalizer: 3.4.0
  - colorama: 0.4.6
  - contourpy: 1.3.1
  - cycler: 0.12.1
  - efficientnet-pytorch: 0.7.1
  - eval-type-backport: 0.2.0
  - filelock: 3.16.1
  - fonttools: 4.55.3
  - frozenlist: 1.5.0
  - fsspec: 2024.10.0
  - grpcio: 1.68.1
  - h2: 4.1.0
  - hpack: 4.0.0
  - huggingface-hub: 0.27.0
  - hyperframe: 6.0.1
  - idna: 3.10
  - importlib-metadata: 8.0.0
  - inflect: 7.3.1
  - jaraco.collections: 5.1.0
  - jaraco.context: 5.3.0
  - jaraco.functools: 4.0.1
  - jaraco.text: 3.12.1
  - jinja2: 3.1.4
  - kiwisolver: 1.4.7
  - lightning: 2.4.0
  - lightning-utilities: 0.11.9
  - markdown: 3.7
  - markupsafe: 3.0.2
  - matplotlib: 3.10.0
  - more-itertools: 10.3.0
  - mpmath: 1.3.0
  - multidict: 6.1.0
  - munch: 4.0.0
  - networkx: 3.4.2
  - numpy: 2.2.0
  - opencv-python: 4.10.0.84
  - opencv-python-headless: 4.10.0.84
  - packaging: 24.2
  - pillow: 10.4.0
  - pip: 24.3.1
  - platformdirs: 4.2.2
  - pretrainedmodels: 0.7.4
  - propcache: 0.2.1
  - protobuf: 5.29.2
  - pycocotools: 2.0.8
  - pycparser: 2.22
  - pydantic: 2.10.4
  - pydantic-core: 2.27.2
  - pyparsing: 3.2.0
  - pysocks: 1.7.1
  - python-dateutil: 2.9.0.post0
  - pytorch-lightning: 2.4.0
  - pyyaml: 6.0.2
  - requests: 2.32.3
  - safetensors: 0.5.0
  - scipy: 1.14.1
  - segmentation-models-pytorch: 0.3.5.dev0
  - setuptools: 75.6.0
  - simsimd: 6.2.1
  - six: 1.17.0
  - stringzilla: 3.11.2
  - sympy: 1.13.1
  - tensorboard: 2.18.0
  - tensorboard-data-server: 0.7.2
  - timm: 1.0.12
  - tomli: 2.0.1
  - torch: 2.5.1
  - torchmetrics: 1.6.0
  - torchvision: 0.20.1
  - tqdm: 4.67.1
  - typeguard: 4.3.0
  - typing-extensions: 4.12.2
  - urllib3: 2.2.3
  - werkzeug: 3.1.3
  - wheel: 0.45.1
  - win-inet-pton: 1.1.0
  - yarl: 1.18.3
  - zipp: 3.19.2
  - zstandard: 0.23.0
• System:
  - OS: Windows
  - architecture:
  - 64bit
  - WindowsPE
  - processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel
  - python: 3.12.8
  - release: 10
  - version: 10.0.19045

More info

No response

[lantiga]

Hey @11philip22 can you show the full imports? I'd like to make sure you're not importing the Trainer and the strategy from different packages, like pytorch_lightning and lightning.

[thomas-keller]

Hi, I also encountered the same error recently trying to run the mnist tune example. For me, it was an issue of pytorch_lightning vs lightning. I replaced pytorch_lightning as pl with lightning.pytorch as pl, and it worked as expected.

Now, why I had pytorch_lightning AND lightning installed is a question only past me can answer.