A library with simple and straightforward tooling for model evaluations and a delightful user experience. At a high level TorchEval:

  1. Contains a rich collection of high performance metric calculations out of the box. We utilize vectorization and GPU acceleration where possible via PyTorch.

  2. Integrates seamlessly with distributed training and tools using torch.distributed

  3. Is designed with extensibility in mind: you have the freedom to easily create your own metrics and leverage our toolkit.

  4. Provides tools for profiling memory and compute requirements for PyTorch based models.



TorchEval can be installed from PyPi via

pip install torcheval

or from github

git clone
cd torcheval
pip install -r requirements.txt
python install


TorchEval provides two interfaces to each metric. If you are working in a single process environment, it is simplest to use metrics from the functional submodule. These can be found in torcheval.metrics.functional.

from torcheval.metrics.functional import binary_f1_score
predictions = model(inputs)
f1_score = binary_f1_score(predictions, targets)

We can use the same metric in the class based route, which provides tools that make computation simple in a multi-process setting. On a single device, you can use the class based metrics as follows:

from torcheval.metrics import BinaryF1Score
predictions = model(inputs)
metric = BinaryF1Score()
metric.update(predictions, targets)
f1_score = metric.compute()

In a multi-process setting, the data from each process must be synchronized to compute the metric across the full dataset. To do this, simply replace metric.compute() with sync_and_compute(metric):

from torcheval.metrics import BinaryF1Score
from torcheval.metrics.toolkit import sync_and_compute
predictions = model(inputs)
metric = BinaryF1Score()
metric.update(predictions, targets)
f1_score = sync_and_compute(metric)

Read more about the class based method in the distributed example.

Further Reading

  • Check out the guides explaining the compute example

  • Check out the distributed example

  • Check out how to make your own metric

Indices and tables

TorchEval Tutorials

TorchEval API


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