{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/6"}],"enrichment":{"capability":"TorchEval provides a collection of PyTorch model metrics with both functional and class-based interfaces, supporting single-process, multi-GPU, and distributed training scenarios.","skillfed_tags":["distributed-training","model-evaluation"],"use_cases":["Compute metrics during training loops with deferred computation across batches using class-based interfaces","Synchronize and aggregate metrics across multiple GPUs or processes in distributed training without manual rank communication","Evaluate model performance on CPU or GPU with a consistent interface that handles device placement automatically","Build custom metrics by extending the class-based interface or using functional metric primitives","Checkpoint and restore metric state during long training runs using the class-based metric API"],"what_it_does":"TorchEval is a metrics library that abstracts away the complexity of computing model evaluation metrics across different training scenarios. It offers both functional interfaces for immediate computation and class-based interfaces that defer computation, the latter being especially useful for accumulating data across multiple batches or synchronizing metrics in distributed settings.\n\nThe library is designed to work on CPU, single GPU, and multi-process or multi-GPU configurations. Its main value lies in simplifying metric synchronization and computation in distributed training, where manual metric aggregation across ranks is error-prone. However, it is currently in pre-alpha (Development Status :: 2 - Pre-Alpha), meaning the API may change and backward compatibility is not guaranteed.","worth_installing":"Yes, with conditions. Install if you are training models in distributed settings and want to avoid manual metric synchronization logic, or if you prefer a unified metric interface across single and multi-GPU scenarios. Avoid if you require stable APIs and backward compatibility guarantees, as the package is pre-alpha and may introduce breaking changes. The low install friction and active maintenance are favorable, but verify that the available metrics match your evaluation needs."},"id":"torcheval","links":{"html":"https://skillfed.io/packages/torcheval","md":"https://skillfed.io/packages/torcheval.md","pypi":"https://pypi.org/project/torcheval/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2023-08-24","license_spdx":null,"license_treatment":"permissive","name":"torcheval","python_support":"supports_current","summary":"A library for providing a simple interface to create new metrics and an easy-to-use toolkit for metric computations and checkpointing."},"popularity":{"monthly_downloads":376719,"position":7128,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.0.7"}