{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/2"}],"enrichment":{"capability":"onnx-ir provides an in-memory intermediate representation for ONNX models that supports the full ONNX specification, enabling graph construction, analysis, and transformation without requiring protobuf after initial conversion.","skillfed_tags":["onnx-tooling","graph-ir","model-optimization"],"use_cases":["Build ONNX models programmatically by constructing graphs through the IR API rather than working directly with protobuf.","Analyze and traverse ONNX model topology to extract information about layers, connections, and data flow.","Transform and optimize ONNX graphs by mutating nodes and values while iterating over the graph structure.","Load and repair invalid ONNX models that cannot be loaded via standard protobuf deserialization.","Integrate ONNX model manipulation into larger ML tooling pipelines without protobuf as a runtime dependency."],"what_it_does":"onnx-ir is an in-memory intermediate representation layer for ONNX models that decouples graph manipulation from protobuf serialization. It provides a Pythonic API for constructing, analyzing, and transforming ONNX computation graphs while supporting the full ONNX specification. The package is designed to handle large models efficiently through memory-mapped external tensors and unified interfaces for different tensor types (numpy arrays, PyTorch tensors, ONNX TensorProto), with no hard limits on tensor size and zero-copy semantics where possible.\n\nThe IR is built on core entities\u2014Model, Graph, Node, Value\u2014that map intuitively to ONNX protobuf concepts but remain independent of the serialization format once loaded. It supports robust mutation with concurrent iterators on the graph, making it suitable for workflows that need to inspect and modify model structure programmatically. The package targets developers building ONNX tooling, model optimization pipelines, or graph-level analysis tools.","worth_installing":"Yes. onnx-ir is actively maintained (released 2026-08-11), has no known vulnerabilities, carries a permissive Apache-2.0 license, and low install friction. It is worth installing if you need to programmatically construct, analyze, or transform ONNX graphs. The IR abstraction is most valuable for tooling and optimization workflows; for simple model inference, standard ONNX Runtime is more direct."},"id":"onnx-ir","links":{"html":"https://skillfed.io/packages/onnx-ir","md":"https://skillfed.io/packages/onnx-ir.md","pypi":"https://pypi.org/project/onnx-ir/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-11","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"onnx-ir","python_support":"supports_current","summary":"Efficient in-memory representation for ONNX"},"popularity":{"monthly_downloads":3113169,"position":2748,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"1.0.0"}