torcheval 0.0.4

A library for providing a simple interface to create new metrics and an easy-to-use toolkit for metric computations and checkpointing.

TorchEval

This library is currently in Alpha and currently does not have a stable release. The API may change and may not be backward compatible. If you have suggestions for improvements, please open a GitHub issue. We'd love to hear your feedback.

A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.

Installing TorchEval

Requires Python >= 3.7 and PyTorch >= 1.11

From pip:

pip install torcheval

For nighly build version

pip install --pre torcheval-nightly

From source:

git clone https://github.com/pytorch-labs/torcheval
cd torcheval
pip install -r requirements.txt
python setup.py install

Quick Start

cd torcheval
python examples/simple_example.py

Using TorchEval

TorchEval can be run on CPU, GPU, and Multi-GPUs/Muti-Nodes.

For the multiple devices usage:

import torch
from torcheval.metrics.toolkit import sync_and_compute
from torcheval.metrics import MulticlassAccuracy

local_rank = int(os.environ["LOCAL_RANK"])
global_rank = int(os.environ["RANK"])
world_size  = int(os.environ["WORLD_SIZE"])

device = torch.device(
    f"cuda:{local_rank}"
    if torch.cuda.is_available() and torch.cuda.device_count() >= world_size
    else "cpu"
)

metric = MulticlassAccuracy(device=device)
num_epochs, num_batches = 4, 8

for epoch in range(num_epochs):
    for i in range(num_batches):
        input = torch.randint(high=5, size=(10,), device=device)
        target = torch.randint(high=5, size=(10,), device=device)

        # metric.update() updates the metric state with new data
        metric.update(input, target)


        # metric.compute() returns metric value from all seen data on the local process.
        local_compute_result = metric.compute()

        # sync_and_compute(metric) returns metric value from all seen data on all processes.
        # It gives the same result as ``metric.compute()`` if it's run on single process.
        global_compute_result = sync_and_compute(metric)

        # The final result is collected by rank 0
        if global_rank == 0:
            print(global_compute_result)

    # metric.reset() cleans up all seen data
    metric.reset()

See the example directory for more examples.

Contributing

We welcome PRs! See the CONTRIBUTING file.

License

TorchEval is BSD licensed, as found in the LICENSE file.