Skip to main content

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

Project description

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/facebookresearch/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().to(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(preds, 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.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torcheval-nightly-2022.8.5.tar.gz (41.0 kB view details)

Uploaded Source

Built Distribution

torcheval_nightly-2022.8.5-py3-none-any.whl (77.0 kB view details)

Uploaded Python 3

File details

Details for the file torcheval-nightly-2022.8.5.tar.gz.

File metadata

  • Download URL: torcheval-nightly-2022.8.5.tar.gz
  • Upload date:
  • Size: 41.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.7.13

File hashes

Hashes for torcheval-nightly-2022.8.5.tar.gz
Algorithm Hash digest
SHA256 5ab851af26761b2ff726caaa8194a17e6253a444944fe011a1c265b7ce373d95
MD5 465bb6c3c778fcb11933ffe660c99a00
BLAKE2b-256 3dd80b8bcb4d3e569ce221104371ecc2eed1c084931ccc6445327e2658ae98a0

See more details on using hashes here.

Provenance

File details

Details for the file torcheval_nightly-2022.8.5-py3-none-any.whl.

File metadata

File hashes

Hashes for torcheval_nightly-2022.8.5-py3-none-any.whl
Algorithm Hash digest
SHA256 85e8f3a71fe236b597b531714b379054ae0c287a937b02067041b1c29b3cb360
MD5 3db9b8dcc1439b25a175476e7edca5ec
BLAKE2b-256 7681ac20c59c1374dae1f143567e2494a5c48850be8b7ff2e5426fd0d43562ff

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page