#####################################
Deploy models into production (basic)
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**Audience**: All users.

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*****************************
Load a checkpoint and predict
*****************************
The easiest way to use a model for predictions is to load the weights using **load_from_checkpoint** found in the LightningModule.

.. code-block:: python

    model = LitModel.load_from_checkpoint("best_model.ckpt")
    model.eval()
    x = torch.randn(1, 64)

    with torch.no_grad():
        y_hat = model(x)

----

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Predict step with your LightningModule
**************************************
Loading a checkpoint and predicting still leaves you with a lot of boilerplate around the predict epoch. The **predict step** in the LightningModule removes this boilerplate.

.. code-block:: python

    class MyModel(LightningModule):
        def predict_step(self, batch, batch_idx, dataloader_idx=0):
            return self(batch)

And pass in any dataloader to the Lightning Trainer:

.. code-block:: python

    data_loader = DataLoader(...)
    model = MyModel()
    trainer = Trainer()
    predictions = trainer.predict(model, data_loader)

----

********************************
Enable complicated predict logic
********************************
When you need to add complicated pre-processing or post-processing logic to your data use the predict step. For example here we do  `Monte Carlo Dropout <https://arxiv.org/pdf/1506.02142.pdf>`_ for predictions:

.. code-block:: python

    class LitMCdropoutModel(L.LightningModule):
        def __init__(self, model, mc_iteration):
            super().__init__()
            self.model = model
            self.dropout = nn.Dropout()
            self.mc_iteration = mc_iteration

        def predict_step(self, batch, batch_idx):
            # enable Monte Carlo Dropout
            self.dropout.train()

            # take average of `self.mc_iteration` iterations
            pred = [self.dropout(self.model(x)).unsqueeze(0) for _ in range(self.mc_iteration)]
            pred = torch.vstack(pred).mean(dim=0)
            return pred

----

****************************
Enable distributed inference
****************************
By using the predict step in Lightning you get free distributed inference using :class:`~lightning.pytorch.callbacks.prediction_writer.BasePredictionWriter`.

.. code-block:: python

    import torch
    from lightning.pytorch.callbacks import BasePredictionWriter


    class CustomWriter(BasePredictionWriter):
        def __init__(self, output_dir, write_interval):
            super().__init__(write_interval)
            self.output_dir = output_dir

        def write_on_epoch_end(self, trainer, pl_module, predictions, batch_indices):
            # this will create N (num processes) files in `output_dir` each containing
            # the predictions of it's respective rank
            torch.save(predictions, os.path.join(self.output_dir, f"predictions_{trainer.global_rank}.pt"))

            # optionally, you can also save `batch_indices` to get the information about the data index
            # from your prediction data
            torch.save(batch_indices, os.path.join(self.output_dir, f"batch_indices_{trainer.global_rank}.pt"))


    # or you can set `write_interval="batch"` and override `write_on_batch_end` to save
    # predictions at batch level
    pred_writer = CustomWriter(output_dir="pred_path", write_interval="epoch")
    trainer = Trainer(accelerator="gpu", strategy="ddp", devices=8, callbacks=[pred_writer])
    model = BoringModel()
    trainer.predict(model, return_predictions=False)