---
title: Supercharge training (intermediate)
description: >-
  Enable advanced training features using Trainer arguments. These are SOTA
  techniques that are automatically integrated into your training loop without
  changes to your code.
canonical: >-
  https://lightning.ai/docs/pytorch/stable/reference/model/build_model_intermediate
---
# Supercharge training (intermediate) {#supercharge-training-intermediate}

## Enable training features {#enable-training-features}

Enable advanced training features using Trainer arguments. These are SOTA techniques that are automatically integrated into your training loop without changes to your code.

```python
# train 1T+ parameter models with DeepSpeed/FSDP
trainer = Trainer(
    devices=4,
    accelerator="gpu",
    strategy="deepspeed_stage_2",
    precision="16-mixed",
 )

# 20+ helpful arguments for rapid idea iteration
trainer = Trainer(
    max_epochs=10,
    min_epochs=5,
    overfit_batches=1
 )

# access the latest state of the art techniques
trainer = Trainer(callbacks=[WeightAveraging(...)])
```

## Extend the Trainer {#extend-the-trainer}

[Video: https://pl-public-data.s3.amazonaws.com/assets_lightning/cb.mp4](https://pl-public-data.s3.amazonaws.com/assets_lightning/cb.mp4)

If you have multiple lines of code with similar functionalities, you can use *callbacks* to easily group them together and toggle all of those lines on or off at the same time.

```python
trainer = Trainer(callbacks=[AWSCheckpoints()])
```

---

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- Parent: [Model](/docs/pytorch/2.6.6/reference/model.md)