How can I build a custom MLModel with a ragged tensor shape?

To build a custom MLModel with a ragged tensor shape, you can leverage the capabilities of Core ML and the new ML Tensor type introduced in the session "Deploy machine learning and AI models on-device with Core ML" at WWDC 2024. Here are the steps and relevant information:

1. Creating Tensors:

   • ML Tensor is a multidimensional array defined by its shape and scalar type. You can create a tensor from an ML shaped array or a nested collection of scalars.
   • Example: Creating a tensor from a nested collection of scalars.

2. Manipulating Tensors:

   • Tensors support a broad range of operations, including element-wise addition, multiplication, and reshaping.
   • You can slice the tensor by indexing into each dimension, similar to Python numerical libraries.

3. Handling Ragged Tensors:

   • While the context does not explicitly mention ragged tensors, you can manage irregular shapes by carefully slicing and reshaping tensors as needed.
   • Ensure that all tensor operations are dispatched asynchronously and materialize the tensor to an ML shaped array before accessing its underlying data.

For more detailed information, you can refer to the session Deploy machine learning and AI models on-device with Core ML (03:20).

Relevant Sessions

1. Deploy machine learning and AI models on-device with Core ML
2. Bring your machine learning and AI models to Apple silicon

These sessions provide comprehensive insights into creating and manipulating tensors, which are essential for handling custom ML models with complex shapes.