Target playbooks

Each playbook is a step-by-step guide for moving a matten workflow to one specific ecosystem. They share a common structure: when to choose (and not choose) the target, how matten concepts map onto it, worked example migrations drawn from the examples, the conversion path, pitfalls, task-scoped positioning notes, and a minimal checklist.

Available now

• ndarray — general Rust N-D arrays; the first stop for dense numeric workloads at scale, with a contract-backed bridge crate (matten-ndarray).
• nalgebra — dense linear algebra: vectors, matrices, decompositions, and solvers.
• Polars / Pandas — dataframe analytics (group-by, joins, pivots, query). matten-data is an on-ramp and will not grow these.
• Candle — ML tensors, training, and device execution — without implying matten is an ML framework.
• NumPy — the Python scientific path, as a manual/conceptual hand-off.

Decision tree

measured dense numeric hot path?
├─ general N-D arrays / BLAS matmul / axis reductions   → ndarray
└─ small/mid dense linear algebra, decompositions       → nalgebra

need LU / QR / SVD / solvers / eigenvalues?             → nalgebra
need group-by / join / pivot / query?                   → Polars (Rust) / Pandas (Python)
need autodiff / training / GPU?                          → Candle / ML framework
already in Python / NumPy ecosystem?                     → NumPy (matten as upstream producer)
small work, ingestion, glue, learning?                   → stay with matten

If you are unsure whether you have outgrown matten at all, start with When to migrate.