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This follows up on a long-standing discussion from the [machine learning working group]: a desire for some way to attach properties to an ML `graph`. The various ML backends implementing this have mechanisms for tweaking a model's behavior based on properties; this `string`-based approach seems the most flexible way to access this. [machine learning working group]: https://github.com/bytecodealliance/meetings/tree/main/machine-learning/
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Why are these attached to the graph not the execution context? |
yamt
reviewed
Jul 28, 2025
| <h4><a name="method_graph_list_properties"></a><code>[method]graph.list-properties: func</code></h4> | ||
| <p>Retrieve the properties of the graph.</p> | ||
| <p>These are metadata about the graph, unique to the graph and the | ||
| ML backend providing it.</p> |
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is this something supposed to be used to query zero_point/scale?
anyway the description seems too abstract. it would be nicer to give a few examples.
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This follows up on a long-standing discussion from the machine learning working group: a desire for some way to attach properties to an ML
graph. The various ML backends implementing this have mechanisms for tweaking a model's behavior based on properties; thisstring-based approach seems the most flexible way to access this.