TypeError with model.train() using R reticulate #2607
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When attempting to integrate several samples with scVI within an R environment, I'm running into an error that I'm pretty stumped about: "Error in py_call_impl(callable, call_args$unnamed, call_args$named) :
TypeError: Cannot cast scalar from dtype('float64') to dtype('int64') according to the rule 'safe'"
Error output
GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs Error in py_call_impl(callable, call_args$unnamed, call_args$named) : TypeError: Cannot cast scalar from dtype('float64') to dtype('int64') according to the rule 'safe' Run `reticulate::py_last_error()` for details. > reticulate::py_last_error() ── Python Exception Message ────────────────────────────────────────────────────────────────────────────────────────────────────────── Traceback (most recent call last): File "_mt19937.pyx", line 180, in numpy.random._mt19937.MT19937._legacy_seeding TypeError: 'float' object cannot be interpreted as an integer During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/scvi/model/base/_training_mixin.py", line 143, in train return runner() File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/scvi/train/_trainrunner.py", line 98, in __call__ self.trainer.fit(self.training_plan, self.data_splitter) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/scvi/train/_trainer.py", line 219, in fit super().fit(*args, **kwargs) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py", line 544, in fit call._call_and_handle_interrupt( File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py", line 44, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py", line 580, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/trainer.py", line 950, in _run call._call_setup_hook(self) # allow user to setup lightning_module in accelerator environment File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py", line 92, in _call_setup_hook _call_lightning_datamodule_hook(trainer, "setup", stage=fn) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/lightning/pytorch/trainer/call.py", line 179, in _call_lightning_datamodule_hook return fn(*args, **kwargs) File "/n/home01/jdownie/.conda/envs/scvi-env/lib/python3.9/site-packages/scvi/dataloaders/_data_splitting.py", line 132, in setup random_state = np.random.RandomState(seed=settings.seed) File "numpy/random/mtrand.pyx", line 185, in numpy.random.mtrand.RandomState.__init__ File "_mt19937.pyx", line 168, in numpy.random._mt19937.MT19937._legacy_seeding File "_mt19937.pyx", line 188, in numpy.random._mt19937.MT19937._legacy_seeding TypeError: Cannot cast scalar from dtype('float64') to dtype('int64') according to the rule 'safe' ── R Traceback ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▆ 1. └─model$train() 2. └─reticulate:::py_call_impl(callable, call_args$unnamed, call_args$named)Versions:
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