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TORCHMETRICS
PyTorch metrics made distributed and scalable
Apache-2.0
ABOUT
Computing metrics like accuracy, F1 score, or mean average precision during distributed training requires aggregating results across GPUs and devices — naive implementations produce incorrect averages and don't handle the synchronization logic. TorchMetrics provides 100+ built-in metrics that automatically handle distributed synchronization, device placement, and multi-batch accumulation, so you get correct results whether training on one GPU or a thousand.
INSTALL
pip install torchmetricsINTEGRATION GUIDE
1. Track accuracy, precision, and recall during multi-GPU training without writing custom distributed aggregation code
2. Compute regression metrics (MAE, RMSE, R²) across distributed validation loops
3. Log per-class metrics for imbalanced classification problems to detect minority-class degradation
4. Build custom domain-specific metrics with automatic distributed handling using the library's base classes
TAGS
pythonpytorchmetricsevaluationdistributeddeep-learningclassificationregression