---
title: Debug your model (advanced)
description: 'Audience: Users who want to debug distributed models.'
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/debug/debugging_advanced'
---
# Debug your model (advanced) {#debug-your-model-advanced}

**Audience**: Users who want to debug distributed models.

## Debug distributed models {#debug-distributed-models}

To debug a distributed model, we recommend you debug it locally by running the distributed version on CPUs:

```python
trainer = Trainer(accelerator="cpu", strategy="ddp", devices=2)
```

On the CPU, you can use [pdb](https://docs.python.org/3/library/pdb.html) or [breakpoint()](https://docs.python.org/3/library/functions.html#breakpoint)
or use regular print statements.

```python
class LitModel(LightningModule):
    def training_step(self, batch, batch_idx):
        debugging_message = ...
        print(f"RANK - {self.trainer.global_rank}: {debugging_message}")

        if self.trainer.global_rank == 0:
            import pdb

            pdb.set_trace()

        # to prevent other processes from moving forward until all processes are in sync
        self.trainer.strategy.barrier()
```

When everything works, switch back to GPU by changing only the accelerator.

```python
trainer = Trainer(accelerator="gpu", strategy="ddp", devices=2)
```

---

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- Parent: [Debug](/docs/pytorch/2.6.6/reference/debug.md)