---
title: GPU training (Basic)
description: >-
  Audience: Users looking to save money and run large models faster using single
  or multiple
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/accelerators/gpu_basic'
---
# GPU training (Basic) {#gpu-training-basic}

**Audience:** Users looking to save money and run large models faster using single or multiple

## What is a GPU? {#what-is-a-gpu}

A Graphics Processing Unit (GPU), is a specialized hardware accelerator designed to speed up mathematical computations used in gaming and deep learning.

## Train on GPUs {#train-on-gpus}

The Trainer will run on all available GPUs by default. Make sure you're running on a machine with at least one GPU.
There's no need to specify any NVIDIA flags as Lightning will do it for you.

```python
# run on as many GPUs as available by default
trainer = Trainer(accelerator="auto", devices="auto", strategy="auto")
# equivalent to
trainer = Trainer()

# run on one GPU
trainer = Trainer(accelerator="gpu", devices=1)
# run on multiple GPUs
trainer = Trainer(accelerator="gpu", devices=8)
# choose the number of devices automatically
trainer = Trainer(accelerator="gpu", devices="auto")
```

> **Note**
>
> Setting `accelerator="gpu"` will also automatically choose the "mps" device on Apple sillicon GPUs.
> If you want to avoid this, you can set `accelerator="cuda"` instead.

### Choosing GPU devices {#choosing-gpu-devices}

You can select the GPU devices using ranges, a list of indices or a string containing
a comma separated list of GPU ids:

```python
# DEFAULT (int) specifies how many GPUs to use per node
Trainer(accelerator="gpu", devices=k)

# Above is equivalent to
Trainer(accelerator="gpu", devices=list(range(k)))

# Specify which GPUs to use (don't use when running on cluster)
Trainer(accelerator="gpu", devices=[0, 1])

# Equivalent using a string
Trainer(accelerator="gpu", devices="0, 1")

# To use all available GPUs put -1 or '-1'
# equivalent to `list(range(torch.cuda.device_count())) and `"auto"`
Trainer(accelerator="gpu", devices=-1)
```

The table below lists examples of possible input formats and how they are interpreted by Lightning.

| `devices` | Type | Parsed | Meaning |
| --- | --- | --- | --- |
| 3 | int | [0, 1, 2] | first 3 GPUs |
| -1 | int | [0, 1, 2, ...] | all available GPUs |
| [0] | list | [0] | GPU 0 |
| [1, 3] | list | [1, 3] | GPU index 1 and 3 (0-based) |
| "3" | str | [0, 1, 2] | first 3 GPUs |
| "1, 3" | str | [1, 3] | GPU index 1 and 3 (0-based) |
| "-1" | str | [0, 1, 2, ...] | all available GPUs |

### Find usable CUDA devices {#find-usable-cuda-devices}

If you want to run several experiments at the same time on your machine, for example for a hyperparameter sweep, then you can
use the following utility function to pick GPU indices that are "accessible", without having to change your code every time.

```python
from lightning.pytorch.accelerators import find_usable_cuda_devices

# Find two GPUs on the system that are not already occupied
trainer = Trainer(accelerator="cuda", devices=find_usable_cuda_devices(2))

from lightning.fabric.accelerators import find_usable_cuda_devices

# Works with Fabric too
fabric = Fabric(accelerator="cuda", devices=find_usable_cuda_devices(2))
```

This is especially useful when GPUs are configured to be in "exclusive compute mode", such that only one process at a time is allowed access to the device.
This special mode is often enabled on server GPUs or systems shared among multiple users.

---

**Navigation**

- Parent: [Accelerators](/docs/pytorch/2.6.6/reference/accelerators.md)