For the complete documentation index, see llms.txt. Markdown versions of all pages are available by appending .md to any URL (e.g. /max/get-started.md).

Python package

max.experimental

Experimental APIs for building, sharding, and running ML workloads.

Built on top of max.graph and max.driver, in three layers that each consume the one below:

nn – the Module base class plus ahead-of-time compilation to a CompiledModel.
functional – a one-function-per-op distributed dispatcher (F.matmul, F.add, …).
sharding – placements, the device mesh, the action data model, a cost model, and pluggable per-op solvers.

The distributed Tensor ties them together.

Example:

from max.experimental import Tensor
from max.experimental import functional as F

x = Tensor.ones((4, 8))
y = F.matmul(x, x.T)

Warning

Experimental. The public API is not stable; names and module structure may change.

The max.experimental package contains eager-execution APIs. These APIs provide a PyTorch-style interface for tensor operations, neural network modules, and functional operations.

Modules​

Modules