Spox makes it easy to construct ONNX models through clean and idiomatic Python
code.
A common application of ONNX is converting models from various frameworks. This
requires replicating their runtime behaviour with ONNX operators. In the past
this has been a major challenge. Based on our experience, we designed Spox from
the ground up to make the process of writing converters (and ONNX models in
general) as easy as possible.
Spox's features include:
- Eager operator validation and type inference
- Errors with Python tracebacks to offending operators
- First-class support for subgraphs (control flow)
- A lean and predictable API
NOTE: If this package has multiple flavors (see below), then use one of them instead of the name specified above. NOTE: This is a Python port. Instead of py312-spox listed in the above command, you can pick from the names under the Packages section.
math/py-spox: Add py-spox 0.16.0
Spox makes it easy to construct ONNX models through clean and idiomatic Python
code.
A common application of ONNX is converting models from various frameworks. This
requires replicating their runtime behaviour with ONNX operators. In the past
this has been a major challenge. Based on our experience, we designed Spox from
the ground up to make the process of writing converters (and ONNX models in
general) as easy as possible.
Spox's features include:
- Eager operator validation and type inference
- Errors with Python tracebacks to offending operators
- First-class support for subgraphs (control flow)
- A lean and predictable API
