# Tune - support for Lightning (not pytorch\_lightning)?

**URL:** <https://discuss.ray.io/t/tune-support-for-lightning-not-pytorch-lightning/13906>\
**Category:** Ray Tune\
**Created:** [March 4, 2024, 6:46pm UTC](https://discuss.ray.io/t/tune-support-for-lightning-not-pytorch-lightning/13906 "2024-03-04T18:46:49Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![nrxszvo](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/nrxszvo/32/5800_2.png) [@nrxszvo](https://discuss.ray.io/u/nrxszvo)\
**Post date:** [March 4, 2024, 6:46pm UTC](https://discuss.ray.io/t/tune-support-for-lightning-not-pytorch-lightning/13906/1 "2024-03-04T18:46:49Z")

</div>

When attempting to use `TuneReportCheckpointCallback` with my `Lightning trainer`, I get an error that seems to be due to a compatibility issue between `Lightning` and `pytorch_lightning`. Is this expected, and are there plans to fully support Lightning in Ray?

To work around the issue, I rewrote most of the `ray.tune.integration.pytorch_lightning` module using Lightning imports instead.

Stack trace of one of the errors I’ve encountered when using `TuneReportCheckpointCallback` with a `Lightning.Trainer:`

````File
   trainer = L.Trainer(
             ^^^^^^^^^^
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/utilities/argparse.py", line 70, in insert_env_defaults
   return fn(self, **kwargs)
          ^^^^^^^^^^^^^^^^^^
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py", line 430, in __init__
   self._callback_connector.on_trainer_init(
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/callback_connector.py", line 79, in on_trainer_init
   _validate_callbacks_list(self.trainer.callbacks)
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/callback_connector.py", line 227, in _validate_callbacks_list
   stateful_callbacks = [cb for cb in callbacks if is_overridden("state_dict", instance=cb)]
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/trainer/connectors/callback_connector.py", line 227, in <listcomp>
   stateful_callbacks = [cb for cb in callbacks if is_overridden("state_dict", instance=cb)]
                                                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 File "/home/paperspace/miniconda3/envs/ncm/lib/python3.11/site-packages/lightning/pytorch/utilities/model_helpers.py", line 42, in is_overridden
   raise ValueError("Expected a parent")
ValueError: Expected a parent```
````

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<div class="post-metadata">

**Author:** ![nrxszvo](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/nrxszvo/32/5800_2.png) [@nrxszvo](https://discuss.ray.io/u/nrxszvo)\
**Post date:** [March 5, 2024, 8:45pm UTC](https://discuss.ray.io/t/tune-support-for-lightning-not-pytorch-lightning/13906/2 "2024-03-05T20:45:32Z")

</div>

I found this example,

[https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html](https://docs.ray.io/en/latest/tune/examples/tune-pytorch-lightning.html)

which uses the `ray.train.lightning` module instead and seems to solve the compatibility issues between `pytorch_lightning` and `Lightning`