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name: backend-and-batch description: "Use POT with NumPy, PyTorch, JAX, TensorFlow, CuPy arrays, backend detection and conversion, optional backend controls, and batched OT/GW solvers." disable-model-invocation: true metadata: disco-role: operating license: MIT

POT Backend and Batch Operations

Use this sub-skill when the task involves POT array backends or many same-shaped OT problems: NumPy baseline arrays, optional PyTorch/JAX/TensorFlow/CuPy arrays, backend discovery and conversion, environment variables that disable optional backend imports, mixed-backend errors, TensorFlow NumPy behavior, ot.batch vectorization, or gradient-memory choices.

Read references/backend-reference.md when deciding how to call get_backend, get_backend_list, to_numpy, backend classes, optional backend extras, disable environment variables, TensorFlow setup, or dtype/device-preserving conversion.

Read references/batch-solvers.md when building batched dist_batch, solve_batch, solve_sample_batch, or solve_gromov_batch workflows, checking verified signatures/defaults, validating plan shapes/marginals, or comparing a batched solve with a looped solve.

Read references/troubleshooting.md when POT raises mixed-backend ValueErrors, optional imports are missing, TensorFlow NumPy behavior is inactive, GPU or CuPy variants are confusing, PyTorch gradient memory is too high, or batch dimensions/weights/methods fail.

Run scripts/backend_batch_smoke.py after installing POT to print detected backend implementations and exercise deterministic NumPy batch-solver fixtures without plotting, downloads, native repo tests, source checkouts, or optional backend assumptions.

Route mathematical OT solver selection to the owning solver sub-skill: core-solvers for ordinary exact/Sinkhorn OT, gromov for GW/FGW modeling, unbalanced-partial for relaxed mass, barycenters for barycenter construction, and sliced-gaussian-large-scale for approximation families. This sub-skill owns backend mechanics and batch vectorization, not choosing the mathematical model.