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GPU training (Basic)

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


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

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.

# 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

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

# 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()))
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]

GPUs 1 and 3

“3”

str

[0, 1, 2]

first 3 GPUs

“1, 3”

str

[1, 3]

GPUs 1 and 3

“-1”

str

[0, 1, 2, …]

all available GPUs

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.

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.


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