Can a single gpu be used for training？

[AIzhiqing]

When I execute python iso/scripts/train_iso.py n_gpus=1 batch_size=2, the error is as follows:

 trainer.fit(model, data_module) 
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 520, in fit
   call._call_and_handle_interrupt(
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 42, in _call_and_handle_interrupt
   return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/strategies/launchers/subprocess_script.py", line 92, in launch
   return function(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 559, in _fit_impl
   self._run(model, ckpt_path=ckpt_path)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 935, in _run
   results = self._run_stage()
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 978, in _run_stage
   self.fit_loop.run()
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 201, in run
   self.advance()
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/fit_loop.py", line 354, in advance
   self.epoch_loop.run(self._data_fetcher)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 133, in run
   self.advance(data_fetcher)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/training_epoch_loop.py", line 218, in advance
   batch_output = self.automatic_optimization.run(trainer.optimizers[0], kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 185, in run
   self._optimizer_step(kwargs.get("batch_idx", 0), closure)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 261, in _optimizer_step
   call._call_lightning_module_hook(
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 142, in _call_lightning_module_hook
   output = fn(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/core/module.py", line 1266, in optimizer_step
   optimizer.step(closure=optimizer_closure)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/core/optimizer.py", line 158, in step
   step_output = self._strategy.optimizer_step(self._optimizer, closure, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/strategies/ddp.py", line 257, in optimizer_step
   optimizer_output = super().optimizer_step(optimizer, closure, model, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/strategies/strategy.py", line 224, in optimizer_step
   return self.precision_plugin.optimizer_step(optimizer, model=model, closure=closure, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 114, in optimizer_step
   return optimizer.step(closure=closure, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/optim/lr_scheduler.py", line 69, in wrapper
   return wrapped(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/optim/optimizer.py", line 280, in wrapper
   out = func(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/optim/optimizer.py", line 33, in _use_grad
   ret = func(self, *args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/optim/adamw.py", line 148, in step
   loss = closure()
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 101, in _wrap_closure
   closure_result = closure()
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 140, in __call__
   self._result = self.closure(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 135, in closure
   self._backward_fn(step_output.closure_loss)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/loops/optimization/automatic.py", line 233, in backward_fn
   call._call_strategy_hook(self.trainer, "backward", loss, optimizer)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 288, in _call_strategy_hook
   output = fn(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/strategies/strategy.py", line 199, in backward
   self.precision_plugin.backward(closure_loss, self.lightning_module, optimizer, *args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/plugins/precision/precision_plugin.py", line 67, in backward
   model.backward(tensor, *args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/pytorch_lightning/core/module.py", line 1055, in backward
   loss.backward(*args, **kwargs)
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/_tensor.py", line 487, in backward
   torch.autograd.backward(
 File "/opt/conda/envs/iso/lib/python3.9/site-packages/torch/autograd/__init__.py", line 200, in backward
   Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: GET was unable to find an engine to execute this computation