{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"Polygraphy is a toolkit for running inference across multiple deep learning backends (TensorRT, ONNX-Runtime, etc.), comparing results, converting models to different formats, and debugging model behavior.","skillfed_tags":["deep-learning-inference","model-debugging","tensorrt"],"use_cases":["Compare inference results across TensorRT and ONNX-Runtime to verify model conversion correctness","Convert trained models to TensorRT engines with post-training quantization for deployment optimization","Debug why a model produces different outputs on different inference backends","Inspect and extract subgraphs from ONNX models for testing or modification","Isolate which TensorRT tactic is causing numerical instability in a model"],"what_it_does":"Polygraphy is NVIDIA's toolkit for prototyping and debugging deep learning model inference. It provides both a Python API and command-line interface that let you run models across multiple inference backends (TensorRT, ONNX-Runtime, and others), compare their outputs to detect discrepancies, and convert models between formats with optional quantization. The toolkit also includes utilities to inspect model structure, extract and modify ONNX subgraphs, and isolate problematic TensorRT tactics.\n\nThe package has no hard Python dependencies, but functionality is modular: each backend (TensorRT, ONNX, etc.) has its own optional requirements. You can either install dependencies manually per backend or enable automatic installation at runtime via environment variable. This design lets you keep a minimal footprint if you only use a subset of backends, or get everything installed on-demand if you prefer convenience.","worth_installing":"Yes. Polygraphy is actively maintained, has no install friction, carries a permissive license, and solves a real problem for anyone working with multiple deep learning inference backends. The modular dependency design means you only pay for what you use. No known vulnerabilities. Install it if you need to prototype, compare, or debug deep learning inference across frameworks."},"id":"polygraphy","links":{"html":"https://skillfed.io/packages/polygraphy","md":"https://skillfed.io/packages/polygraphy.md","pypi":"https://pypi.org/project/polygraphy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-07","license_spdx":null,"license_treatment":"permissive","name":"polygraphy","python_support":"supports_current","summary":"Polygraphy: A Deep Learning Inference Prototyping and Debugging Toolkit"},"popularity":{"monthly_downloads":227487,"position":9178,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.53.4"}