{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Counts floating-point operations (MACs) and parameters in PyTorch neural network models to profile computational complexity.","skillfed_tags":["model-profiling","pytorch-tools"],"use_cases":["Compare computational complexity across different neural network architectures before training.","Profile existing models to identify bottlenecks and guide optimization efforts.","Estimate deployment feasibility by checking MACs and parameter counts against hardware constraints.","Benchmark model efficiency for resource-constrained deployment scenarios.","Validate custom layers by defining counting rules for non-standard torch modules."],"what_it_does":"THOP is a PyTorch profiling tool that measures the computational cost of neural network models by counting multiply-accumulate operations (MACs) and model parameters. It works by instrumenting torch models during a forward pass to track operations, then reporting aggregate statistics.\n\nThe package is designed for model developers and researchers who need to understand the computational footprint of their networks\u2014useful when comparing architectures, optimizing for deployment, or working within compute budgets. It includes built-in counting rules for standard layers and allows custom counting logic for third-party modules. The `clever_format` utility provides readable output formatting.","worth_installing":"Yes, if you are actively profiling torch models and can tolerate dormant maintenance. The package is stable for its narrow use case and has no known vulnerabilities, but verify compatibility with your torch version before relying on it in production. Consider alternatives if you need ongoing support or features for very recent model architectures."},"id":"thop","links":{"html":"https://skillfed.io/packages/thop","md":"https://skillfed.io/packages/thop.md","pypi":"https://pypi.org/project/thop/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2022-09-07","license_spdx":null,"license_treatment":"permissive","name":"thop","python_support":"unspecified","summary":"A tool to count the FLOPs of PyTorch model."},"popularity":{"monthly_downloads":1035556,"position":4462,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.1.1.post2209072238"}