torcheval
A library for providing a simple interface to create new metrics and an easy-to-use toolkit for metric computations and checkpointing.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.7 and PyTorch >= 1.11
- Low install friction with a single runtime dependency.
- The package is actively maintained with recent commits, though it remains in pre-alpha status with an API that may change and is not guaranteed to be backward compatible.
License · maintenance · safety
BSD-3 (permissive) — BSD-3 licensed under a permissive license, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
last release 2023-08-24 (1086 days) · last repo commit 2026-08-14 · 247 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 376,719 downloads/mo, #7,128 on PyPI
Alternatives
Verify before relying
pip install torcheval
from torcheval.metrics import MulticlassAccuracy
metric = MulticlassAccuracy()
metric.update(outputs, target)
result = metric.compute()- Whether the pre-alpha status and lack of stable release significantly impacts production readiness beyond API instability
- Performance characteristics and overhead of metric computation compared to manual implementations
- Completeness of the metric collection relative to common evaluation needs
- Whether typing-extensions dependency introduces any compatibility constraints
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
torcheval on PyPI
Before you install
Low install friction with a single runtime dependency. The package is actively maintained with recent commits, though it remains in pre-alpha status with an API that may change and is not guaranteed to be backward compatible.
Requires Python >= 3.7 and PyTorch >= 1.11
License in practice
BSD-3 licensed under a permissive license, allowing commercial and private use with minimal restrictions beyond attribution and liability disclaimers.
Quickstart
pip install torcheval
from torcheval.metrics import MulticlassAccuracy
metric = MulticlassAccuracy()
metric.update(outputs, target)
result = metric.compute()Verify before relying
- Whether the pre-alpha status and lack of stable release significantly impacts production readiness beyond API instability
- Performance characteristics and overhead of metric computation compared to manual implementations
- Completeness of the metric collection relative to common evaluation needs
- Whether typing-extensions dependency introduces any compatibility constraints
Package facts
| License | BSD-3 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetyping-extensions |
| Maintenance | Actively maintained 1,086 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 376,719 / month, #7,128 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 2 - Pre-AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.7Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: torcheval-0.0.7-py3-none-any.whl
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See also torchmetrics · torch · torchinfo · torchrunx · torch-ema · mmengine · pytorch-ignite · torchrl