# ONNX IR An in-memory IR that supports the full ONNX spec, designed for graph construction, analysis and transformation. ## Features ✨ - Full ONNX spec support: all valid models representable by ONNX protobuf, and a subset of invalid models (so you can load and fix them). - Low memory footprint: mmap'ed external tensors; unified interface for ONNX TensorProto, Numpy arrays and PyTorch Tensors etc. No tensor size limitation. Zero copies. - Straightforward access patterns: Access value information and traverse the graph topology at ease. - Robust mutation: Create as many iterators as you like on the graph while mutating it. - Speed: Performant graph manipulation, serialization/deserialization to Protobuf. - Pythonic and familiar APIs: Classes define Pythonic apis and still map to ONNX protobuf concepts in an intuitive way. - No protobuf dependency: The IR does not require protobuf once the model is converted to the IR representation, decoupling from the serialization format. ## Get started ```{toctree} :maxdepth: 1 Overview <self> getting_started tensors api/index ```