Mojo package

# graph

APIs to build inference graphs for MAX Engine.

The MAX Graph API provides a low-level programming interface for high-performance inference graphs written in Mojo. It's an API for graph-building only, and it does not implement support for training.

To get started, you need to instantiate a
`Graph`

and specify its input
and output shapes. Then build a sequence of ops, using ops provided in the
`graph.ops`

package or using your own
custom ops, and add them to the graph by setting the output op(s) with
`Graph.output()`

.

For example:

`from max.graph import Graph, TensorType, ops`

from max.tensor import Tensor, TensorShape

def build_model() -> Graph:

var graph = Graph(

in_types=TensorType(DType.float32, 2, 6),

out_types=TensorType(DType.float32, 2, 1),

)

var matmul_constant_value = Tensor[DType.float32](TensorShape(6, 1), 0.15)

var matmul_constant = graph.constant(matmul_constant_value)

var matmul = graph[0] @ matmul_constant

var relu = ops.elementwise.relu(matmul)

var softmax = ops.softmax(relu)

graph.output(softmax)

return graph

You can then load the `Graph`

into MAX Engine with
`InferenceSession.load()`

.

For more detail, see the tutorial about how to build a graph with MAX Graph.

## Packagesβ

- β
`checkpoint`

: APIs to save and load checkpoints for MAX graphs. - β
`ops`

: Implements various ops used when building a graph. - β
`quantization`

: APIs to quantize graph tensors.

## Modulesβ

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