)```
````
---
### Author:  [@cane11](https://discourse.scverse.org/u/cane11)
#### Post date: [January 11, 2025, 9:56am UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/2 "2025-01-11T09:56:13Z")
Hi, your min\_genes filter is very low with 3. afterwards you perform hvg filtering to few genes. Can you please check the count distribution after this step. Especially, whether you end up with any cells with zero counts. These are not supported in scVI. You can perform the filtering to 3 genes after hvg filtering. However results are usually bad for cells with less than ~500 counts (for standard 10X sequencing on ‚normal‘ cells.
---
### Author:  [@Slack90](https://discourse.scverse.org/u/Slack90)
#### Post date: [January 11, 2025, 4:04pm UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/3 "2025-01-11T16:04:49Z")
Hi,
Thanks for your reply. I checked the RNAcount and it looks good to me, and there is no 0 value:
I also tried to perform only hvg filtering:
```auto
concatenated_anndata.obs.groupby('dataset').count()
concatenated_anndata.layers["counts"] = concatenated_anndata.X.copy() # preserve counts
sc.pp.normalize_total(concatenated_anndata, target_sum=1e4)
sc.pp.log1p(concatenated_anndata)
concatenated_anndata.raw = concatenated_anndata # freeze the state in `.raw`
sc.pp.highly_variable_genes(concatenated_anndata,n_top_genes=10000,subset=True,layer="counts",batch_key="dataset")
```
And again I checked the distribution of RNA count before initializing the model and there is no zero values. However I still get the same error when starting the model…
---
### Author:  [@ori-kron-wis](https://discourse.scverse.org/u/ori-kron-wis)
#### Post date: [January 12, 2025, 11:05pm UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/4 "2025-01-12T23:05:08Z")
Hey @Slack90
Seems your input is ok.
the issue might be due to exploding gradients during training, you can set gradient clipping in the train function and it should help, e.g:
model.train(…,gradient\_clip\_val=1)
the default is 0.
of course that might also affect the results. there is no rule of thumb on the exact number needed, but trial and error.
Also run with early stopping and large max epochs.
---
### Author:  [@Slack90](https://discourse.scverse.org/u/Slack90)
#### Post date: [January 13, 2025, 4:18pm UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/5 "2025-01-13T16:18:50Z")
Hi @ori-kron-wis,
Thanks for the reply. I ran it again with gradient\_clip\_val =1 (I also tried =10 and other values). I also added early stoppings and large max epochs as follows:
````auto
model.train(gradient_clip_val=1, max_epochs=4000, early_stopping=True)```
````
But I get the same error
```auto
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[23], line 2
1 # model.train(accelerator="gpu", devices="auto", gradient_clip_val=10)
----> 2 model.train(gradient_clip_val=1, max_epochs=400, early_stopping=True)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/model/base/_training_mixin.py:145, in UnsupervisedTrainingMixin.train(self, max_epochs, accelerator, devices, train_size, validation_size, shuffle_set_split, load_sparse_tensor, batch_size, early_stopping, datasplitter_kwargs, plan_kwargs, datamodule, **trainer_kwargs)
133 trainer_kwargs[es] = (
134 early_stopping if es not in trainer_kwargs.keys() else trainer_kwargs[es]
135 )
136 runner = self._train_runner_cls(
137 self,
138 training_plan=training_plan,
(...)
143 **trainer_kwargs,
144 )
--> 145 return runner()
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/train/_trainrunner.py:96, in TrainRunner. __call__ (self)
93 if hasattr(self.data_splitter, "n_val"):
94 self.training_plan.n_obs_validation = self.data_splitter.n_val
---> 96 self.trainer.fit(self.training_plan, self.data_splitter)
97 self._update_history()
99 # data splitter only gets these attrs after fit
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/train/_trainer.py:201, in Trainer.fit(self, *args, **kwargs)
195 if isinstance(args[0], PyroTrainingPlan):
196 warnings.filterwarnings(
197 action="ignore",
198 category=UserWarning,
199 message="`LightningModule.configure_optimizers` returned `None`",
200 )
--> 201 super().fit(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:538, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
536 self.state.status = TrainerStatus.RUNNING
537 self.training = True
--> 538 call._call_and_handle_interrupt(
539 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path
540 )
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:47, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs)
45 if trainer.strategy.launcher is not None:
46 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
---> 47 return trainer_fn(*args, **kwargs)
49 except _TunerExitException:
50 _call_teardown_hook(trainer)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:574, in Trainer._fit_impl(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)
567 assert self.state.fn is not None
568 ckpt_path = self._checkpoint_connector._select_ckpt_path(
569 self.state.fn,
570 ckpt_path,
571 model_provided=True,
572 model_connected=self.lightning_module is not None,
573 )
--> 574 self._run(model, ckpt_path=ckpt_path)
576 assert self.state.stopped
577 self.training = False
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:981, in Trainer._run(self, model, ckpt_path)
976 self._signal_connector.register_signal_handlers()
978 # ----------------------------
979 # RUN THE TRAINER
980 # ----------------------------
--> 981 results = self._run_stage()
983 # ----------------------------
984 # POST-Training CLEAN UP
985 # ----------------------------
986 log.debug(f"{self. __class__. __name__ }: trainer tearing down")
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/trainer.py:1025, in Trainer._run_stage(self)
1023 self._run_sanity_check()
1024 with torch.autograd.set_detect_anomaly(self._detect_anomaly):
-> 1025 self.fit_loop.run()
1026 return None
1027 raise RuntimeError(f"Unexpected state {self.state}")
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:205, in _FitLoop.run(self)
203 try:
204 self.on_advance_start()
--> 205 self.advance()
206 self.on_advance_end()
207 self._restarting = False
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/fit_loop.py:363, in _FitLoop.advance(self)
361 with self.trainer.profiler.profile("run_training_epoch"):
362 assert self._data_fetcher is not None
--> 363 self.epoch_loop.run(self._data_fetcher)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/training_epoch_loop.py:140, in _TrainingEpochLoop.run(self, data_fetcher)
138 while not self.done:
139 try:
--> 140 self.advance(data_fetcher)
141 self.on_advance_end(data_fetcher)
142 self._restarting = False
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/training_epoch_loop.py:250, in _TrainingEpochLoop.advance(self, data_fetcher)
247 with trainer.profiler.profile("run_training_batch"):
248 if trainer.lightning_module.automatic_optimization:
249 # in automatic optimization, there can only be one optimizer
--> 250 batch_output = self.automatic_optimization.run(trainer.optimizers[0], batch_idx, kwargs)
251 else:
252 batch_output = self.manual_optimization.run(kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/automatic.py:190, in _AutomaticOptimization.run(self, optimizer, batch_idx, kwargs)
183 closure()
185 # ------------------------------
186 # BACKWARD PASS
187 # ------------------------------
188 # gradient update with accumulated gradients
189 else:
--> 190 self._optimizer_step(batch_idx, closure)
192 result = closure.consume_result()
193 if result.loss is None:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/automatic.py:268, in _AutomaticOptimization._optimizer_step(self, batch_idx, train_step_and_backward_closure)
265 self.optim_progress.optimizer.step.increment_ready()
267 # model hook
--> 268 call._call_lightning_module_hook(
269 trainer,
270 "optimizer_step",
271 trainer.current_epoch,
272 batch_idx,
273 optimizer,
274 train_step_and_backward_closure,
275 )
277 if not should_accumulate:
278 self.optim_progress.optimizer.step.increment_completed()
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:167, in _call_lightning_module_hook(trainer, hook_name, pl_module, *args, **kwargs)
164 pl_module._current_fx_name = hook_name
166 with trainer.profiler.profile(f"[LightningModule]{pl_module. __class__. __name__ }.{hook_name}"):
--> 167 output = fn(*args, **kwargs)
169 # restore current_fx when nested context
170 pl_module._current_fx_name = prev_fx_name
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/core/module.py:1306, in LightningModule.optimizer_step(self, epoch, batch_idx, optimizer, optimizer_closure)
1275 def optimizer_step(
1276 self,
1277 epoch: int,
(...)
1280 optimizer_closure: Optional[Callable[[], Any]] = None,
1281 ) -> None:
1282 r"""Override this method to adjust the default way the :class:`~lightning.pytorch.trainer.trainer.Trainer` calls
1283 the optimizer.
1284
(...)
1304
1305 """
-> 1306 optimizer.step(closure=optimizer_closure)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/core/optimizer.py:153, in LightningOptimizer.step(self, closure, **kwargs)
150 raise MisconfigurationException("When `optimizer.step(closure)` is called, the closure should be callable")
152 assert self._strategy is not None
--> 153 step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
155 self._on_after_step()
157 return step_output
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/strategies/strategy.py:238, in Strategy.optimizer_step(self, optimizer, closure, model, **kwargs)
236 # TODO(fabric): remove assertion once strategy's optimizer_step typing is fixed
237 assert isinstance(model, pl.LightningModule)
--> 238 return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/plugins/precision/precision.py:122, in Precision.optimizer_step(self, optimizer, model, closure, **kwargs)
120 """Hook to run the optimizer step."""
121 closure = partial(self._wrap_closure, model, optimizer, closure)
--> 122 return optimizer.step(closure=closure, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/optim/optimizer.py:487, in Optimizer.profile_hook_step..wrapper(*args, **kwargs)
482 else:
483 raise RuntimeError(
484 f"{func} must return None or a tuple of (new_args, new_kwargs), but got {result}."
485 )
--> 487 out = func(*args, **kwargs)
488 self._optimizer_step_code()
490 # call optimizer step post hooks
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/optim/optimizer.py:91, in _use_grad_for_differentiable.._use_grad(self, *args, **kwargs)
89 torch.set_grad_enabled(self.defaults["differentiable"])
90 torch._dynamo.graph_break()
---> 91 ret = func(self, *args, **kwargs)
92 finally:
93 torch._dynamo.graph_break()
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/optim/adam.py:202, in Adam.step(self, closure)
200 if closure is not None:
201 with torch.enable_grad():
--> 202 loss = closure()
204 for group in self.param_groups:
205 params_with_grad: List[Tensor] = []
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/plugins/precision/precision.py:108, in Precision._wrap_closure(self, model, optimizer, closure)
95 def _wrap_closure(
96 self,
97 model: "pl.LightningModule",
98 optimizer: Steppable,
99 closure: Callable[[], Any],
100 ) -> Any:
101 """This double-closure allows makes sure the ``closure`` is executed before the ``on_before_optimizer_step``
102 hook is called.
103
(...)
106
107 """
--> 108 closure_result = closure()
109 self._after_closure(model, optimizer)
110 return closure_result
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/automatic.py:144, in Closure. __call__ (self, *args, **kwargs)
142 @override
143 def __call__ (self, *args: Any, **kwargs: Any) -> Optional[Tensor]:
--> 144 self._result = self.closure(*args, **kwargs)
145 return self._result.loss
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/utils/_contextlib.py:116, in context_decorator..decorate_context(*args, **kwargs)
113 @functools.wraps(func)
114 def decorate_context(*args, **kwargs):
115 with ctx_factory():
--> 116 return func(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/automatic.py:129, in Closure.closure(self, *args, **kwargs)
126 @override
127 @torch.enable_grad()
128 def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
--> 129 step_output = self._step_fn()
131 if step_output.closure_loss is None:
132 self.warning_cache.warn("`training_step` returned `None`. If this was on purpose, ignore this warning...")
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/loops/optimization/automatic.py:317, in _AutomaticOptimization._training_step(self, kwargs)
306 """Performs the actual train step with the tied hooks.
307
308 Args:
(...)
313
314 """
315 trainer = self.trainer
--> 317 training_step_output = call._call_strategy_hook(trainer, "training_step", *kwargs.values())
318 self.trainer.strategy.post_training_step() # unused hook - call anyway for backward compatibility
320 if training_step_output is None and trainer.world_size > 1:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/trainer/call.py:319, in _call_strategy_hook(trainer, hook_name, *args, **kwargs)
316 return None
318 with trainer.profiler.profile(f"[Strategy]{trainer.strategy. __class__. __name__ }.{hook_name}"):
--> 319 output = fn(*args, **kwargs)
321 # restore current_fx when nested context
322 pl_module._current_fx_name = prev_fx_name
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/lightning/pytorch/strategies/strategy.py:390, in Strategy.training_step(self, *args, **kwargs)
388 if self.model != self.lightning_module:
389 return self._forward_redirection(self.model, self.lightning_module, "training_step", *args, **kwargs)
--> 390 return self.lightning_module.training_step(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/train/_trainingplans.py:350, in TrainingPlan.training_step(self, batch, batch_idx)
348 self.loss_kwargs.update({"kl_weight": kl_weight})
349 self.log("kl_weight", kl_weight, on_step=True, on_epoch=False)
--> 350 _, _, scvi_loss = self.forward(batch, loss_kwargs=self.loss_kwargs)
351 self.log(
352 "train_loss",
353 scvi_loss.loss,
(...)
356 sync_dist=self.use_sync_dist,
357 )
358 self.compute_and_log_metrics(scvi_loss, self.train_metrics, "train")
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/train/_trainingplans.py:280, in TrainingPlan.forward(self, *args, **kwargs)
278 def forward(self, *args, **kwargs):
279 """Passthrough to the module's forward method."""
--> 280 return self.module(
281 *args,
282 **kwargs,
283 get_inference_input_kwargs={"full_forward_pass": not self.update_only_decoder},
284 )
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_decorators.py:32, in auto_move_data..auto_transfer_args(self, *args, **kwargs)
30 # decorator only necessary after training
31 if self.training:
---> 32 return fn(self, *args, **kwargs)
34 device = list({p.device for p in self.parameters()})
35 if len(device) > 1:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_base_module.py:207, in BaseModuleClass.forward(self, tensors, get_inference_input_kwargs, get_generative_input_kwargs, inference_kwargs, generative_kwargs, loss_kwargs, compute_loss)
176 @auto_move_data
177 def forward(
178 self,
(...)
185 compute_loss=True,
186 ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor, LossOutput]:
187 """Forward pass through the network.
188
189 Parameters
(...)
205 another return value.
206 """
--> 207 return _generic_forward(
208 self,
209 tensors,
210 inference_kwargs,
211 generative_kwargs,
212 loss_kwargs,
213 get_inference_input_kwargs,
214 get_generative_input_kwargs,
215 compute_loss,
216 )
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_base_module.py:747, in _generic_forward(module, tensors, inference_kwargs, generative_kwargs, loss_kwargs, get_inference_input_kwargs, get_generative_input_kwargs, compute_loss)
744 get_inference_input_kwargs.pop("full_forward_pass", None)
746 inference_inputs = module._get_inference_input(tensors, **get_inference_input_kwargs)
--> 747 inference_outputs = module.inference( **inference_inputs,** inference_kwargs)
748 generative_inputs = module._get_generative_input(
749 tensors, inference_outputs, **get_generative_input_kwargs
750 )
751 generative_outputs = module.generative( **generative_inputs,** generative_kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_decorators.py:32, in auto_move_data..auto_transfer_args(self, *args, **kwargs)
30 # decorator only necessary after training
31 if self.training:
---> 32 return fn(self, *args, **kwargs)
34 device = list({p.device for p in self.parameters()})
35 if len(device) > 1:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_base_module.py:308, in BaseMinifiedModeModuleClass.inference(self, *args, **kwargs)
306 return self._cached_inference(*args, **kwargs)
307 else:
--> 308 return self._regular_inference(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/base/_decorators.py:32, in auto_move_data..auto_transfer_args(self, *args, **kwargs)
30 # decorator only necessary after training
31 if self.training:
---> 32 return fn(self, *args, **kwargs)
34 device = list({p.device for p in self.parameters()})
35 if len(device) > 1:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/module/_vae.py:386, in VAE._regular_inference(self, x, batch_index, cont_covs, cat_covs, n_samples)
384 qz, z = self.z_encoder(encoder_input, *categorical_input)
385 else:
--> 386 qz, z = self.z_encoder(encoder_input, batch_index, *categorical_input)
388 ql = None
389 if not self.use_observed_lib_size:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/nn/modules/module.py:1736, in Module._wrapped_call_impl(self, *args, **kwargs)
1734 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1735 else:
-> 1736 return self._call_impl(*args, **kwargs)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/nn/modules/module.py:1747, in Module._call_impl(self, *args, **kwargs)
1742 # If we don't have any hooks, we want to skip the rest of the logic in
1743 # this function, and just call forward.
1744 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1745 or _global_backward_pre_hooks or _global_backward_hooks
1746 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747 return forward_call(*args, **kwargs)
1749 result = None
1750 called_always_called_hooks = set()
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/scvi/nn/_base_components.py:286, in Encoder.forward(self, x, *cat_list)
284 q_m = self.mean_encoder(q)
285 q_v = self.var_activation(self.var_encoder(q)) + self.var_eps
--> 286 dist = Normal(q_m, q_v.sqrt())
287 latent = self.z_transformation(dist.rsample())
288 if self.return_dist:
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/distributions/normal.py:59, in Normal. __init__ (self, loc, scale, validate_args)
57 else:
58 batch_shape = self.loc.size()
---> 59 super(). __init__ (batch_shape, validate_args=validate_args)
File ~/.conda/envs/MyEnv/lib/python3.11/site-packages/torch/distributions/distribution.py:71, in Distribution. __init__ (self, batch_shape, event_shape, validate_args)
69 valid = constraint.check(value)
70 if not valid.all():
---> 71 raise ValueError(
72 f"Expected parameter {param} "
73 f"({type(value). __name__ } of shape {tuple(value.shape)}) "
74 f"of distribution {repr(self)} "
75 f"to satisfy the constraint {repr(constraint)}, "
76 f"but found invalid values:\n{value}"
77 )
78 super(). __init__ ()
ValueError: Expected parameter loc (Tensor of shape (128, 10)) of distribution Normal(loc: torch.Size([128, 10]), scale: torch.Size([128, 10])) to satisfy the constraint Real(), but found invalid values:
tensor([[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]], grad_fn=)
```
---
### Author:  [@cane11](https://discourse.scverse.org/u/cane11)
#### Post date: [January 13, 2025, 8:21pm UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/6 "2025-01-13T20:21:06Z")
Hi, to me it still looks like it doesn’t even train one step (no progress bar). This only happens if the initialization is off. To help you with this, access to the dataset is necessary (count filtering would be the most common issue but you checked it). If training fails latter during training (model.history will contain losses for several epochs) other tricks might be helpful described here: [Frequently asked questions — scvi-tools](https://docs.scvi-tools.org/en/latest/faq.html).
---
### Author:  [@cane11](https://discourse.scverse.org/u/cane11)
#### Post date: [January 15, 2025, 11:46pm UTC](https://discourse.scverse.org/t/model-cannot-be-initialized-and-return-error/3413/7 "2025-01-15T23:46:22Z")
If others look here, it turned out the count data was actually scaled. So it was a dataset problem and not an scvi-tools problem.