Lightning-AI/torchmetrics

Lightning-AI/torchmetrics

Description: Machine learning metrics for distributed, scalable PyTorch applications.

Language: Python

License: Apache-2.0

Stars: 2442

Forks: 492

Open issues: 139

Created: 2020-12-22T20:02:42Z

Pushed: 2026-06-10T21:10:28Z

Default branch: master

Fork: no

Archived: no

README:

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Installation

Simple installation from PyPI

pip install torchmetrics

Other installations

Install using conda

conda install -c conda-forge torchmetrics

Install using uv

uv add torchmetrics

Pip from source

# with git
pip install git+https://github.com/Lightning-AI/torchmetrics.git@release/stable

Pip from archive

pip install https://github.com/Lightning-AI/torchmetrics/archive/refs/heads/release/stable.zip

Extra dependencies for specialized metrics:

pip install torchmetrics[audio]
pip install torchmetrics[image]
pip install torchmetrics[text]
pip install torchmetrics[all] # install all of the above

Install latest developer version

pip install https://github.com/Lightning-AI/torchmetrics/archive/master.zip

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What is TorchMetrics

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility
• Reduces boilerplate
• Automatic accumulation over batches
• Metrics optimized for distributed-training
• Automatic synchronization between multiple devices

You can use TorchMetrics with any PyTorch model or with PyTorch Lightning to enjoy additional features such as:

• Module metrics are automatically placed on the correct device.
• Native support for logging metrics in Lightning to reduce even more boilerplate.

Using TorchMetrics

Module metrics

The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices!

• Automatic accumulation over multiple batches
• Automatic synchronization between multiple devices
• Metric arithmetic

This can be run on CPU, single GPU or multi-GPUs!

For the single GPU/CPU case:

import torch

# import our library
import torchmetrics

# initialize metric
metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5)

# move the metric to device you want computations to take place
device = "cuda" if torch.cuda.is_available() else "cpu"
metric.to(device)

n_batches = 10
for i in range(n_batches):
# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1).to(device)
target = torch.randint(5, (10,)).to(device)

# metric on current batch
acc = metric(preds, target)
print(f"Accuracy on batch {i}: {acc}")

# metric on all batches using custom accumulation
acc = metric.compute()
print(f"Accuracy on all data: {acc}")

Module metric usage remains the same when using multiple GPUs or multiple nodes.

Example using DDP

import os
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
from torch import nn
from torch.nn.parallel import DistributedDataParallel as DDP
import torchmetrics

def metric_ddp(rank, world_size):
os.environ["MASTER_ADDR"] = "localhost"
os.environ["MASTER_PORT"] = "12355"

# create default process group
dist.init_process_group("gloo", rank=rank, world_size=world_size)

# initialize model
metric = torchmetrics.classification.Accuracy(task="multiclass", num_classes=5)

# define a model and append your metric to it
# this allows metric states to be placed on correct accelerators when
# .to(device) is called on the model
model = nn.Linear(10, 10)
model.metric = metric
model = model.to(rank)

# initialize DDP
model = DDP(model, device_ids=[rank])

n_epochs = 5
# this shows iteration over multiple training epochs
for n in range(n_epochs):
# this will be replaced by a DataLoader with a DistributedSampler
n_batches = 10
for i in range(n_batches):
# simulate a classification problem
preds = torch.randn(10, 5).softmax(dim=-1)
target = torch.randint(5, (10,))

# metric on current batch
acc = metric(preds, target)
if rank == 0: # print only for rank 0
print(f"Accuracy on batch {i}: {acc}")

# metric on all batches and all accelerators using custom accumulation
# accuracy is same across both accelerators
acc = metric.compute()
print(f"Accuracy on all data: {acc}, accelerator rank: {rank}")

# Resetting internal state such that metric ready for new data
metric.reset()

# cleanup
dist.destroy_process_group()

if __name__ == "__main__":
world_size = 2 # number of gpus to parallelize over
mp.spawn(metric_ddp, args=(world_size,), nprocs=world_size, join=True)

Implementing your own Module metric

Implementing your own metric is as easy as subclassing an `torch.nn.Module`. Simply, subclass torchmetrics.Metric and just implement the update and compute methods:

import torch
from torchmetrics import Metric

class MyAccuracy(Metric):
def __init__(self):
# remember to call super
super().__init__()
# call `self.add_state`for every…