> ## Documentation Index
> Fetch the complete documentation index at: https://docs.beam.cloud/llms.txt
> Use this file to discover all available pages before exploring further.

# Saving Model Checkpoints

> Using Persistent Volumes to save and load model weights.

When training an ML model, you might want to periodically save a checkpoint with the model weights. You can use a [Persistent Volume](/v2/data/volume) to save checkpoints and retrieve them in subsequent runs.

In this example, we'll demonstrate saving and loading a model checkpoint using PyTorch.

### Mounting a Persistent Volume

The first thing you'll do is mount a Persistent Volume to the Beam runtime in order to save your model weights.

Add the following line to the file with your `app.py`:

```python theme={null}
app.Mount.PersistentVolume(name="saved_models", path="./saved_models")
```

### Saving model checkpoints

During your training loop, you can call `torch.save()` to dump the model weights to a file path. In this case, the file path is a Persistent Volume you've defined in `app.py`.

```python theme={null}
def save_model_weights():
    model = MyModel()

    PERSISTENT_VOLUME_PATH = "./saved_models/cifar_net.pth"
    # Save model to persistent volume
    torch.save(model.state_dict(), PERSISTENT_VOLUME_PATH)
```

### Loading model checkpoints

You can load model weights using the `load_state_dict()` method, by passing in the Persistent Volume path from the previous step.

```python theme={null}
def load_model_weights():
    model = MyModel()

    PERSISTENT_VOLUME_PATH = "./saved_models/cifar_net.pth"
    # Load model from persistent volume
    model.load_state_dict(torch.load(PERSISTENT_VOLUME_PATH))

    saved_model = model.eval()
    print(saved_model)
```