viam.services.mlmodel.mlmodel

Module Contents

Classes

MLModel

MLModel represents a Machine Learning Model service.

class viam.services.mlmodel.mlmodel.MLModel(name: str)[source]

Bases: viam.services.service_base.ServiceBase

MLModel represents a Machine Learning Model service.

This acts as an abstract base class for any drivers representing specific arm implementations. This cannot be used on its own. If the __init__() function is overridden, it must call the super().__init__() function.

SUBTYPE: Final
abstract async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: float | None) Dict[str, numpy.typing.NDArray][source]

Take an already ordered input tensor as an array, make an inference on the model, and return an output tensor map.

import numpy as np

my_mlmodel = MLModelClient.from_robot(robot=robot, name="my_mlmodel_service")

nd_array = np.array([1, 2, 3], dtype=np.float64)
input_tensors = {"0": nd_array}

output_tensors = await my_mlmodel.infer(input_tensors)
Parameters:

input_tensors (Dict[str, NDArray]) – A dictionary of input flat tensors as specified in the metadata

Returns:

A dictionary of output flat tensors as specified in the metadata

Return type:

Dict[str, NDArray]

abstract async metadata(*, timeout: float | None) viam.proto.service.mlmodel.Metadata[source]

Get the metadata (such as name, type, expected tensor/array shape, inputs, and outputs) associated with the ML model.

my_mlmodel = MLModelClient.from_robot(robot=robot, name="my_mlmodel_service")

metadata = await my_mlmodel.metadata()
Returns:

The metadata

Return type:

Metadata

classmethod from_robot(robot: viam.robot.client.RobotClient, name: str) typing_extensions.Self

Get the service named name from the provided robot.

async def connect() -> ViamClient:
    # Replace "<API-KEY>" (including brackets) with your API key and "<API-KEY-ID>" with your API key ID
    dial_options = DialOptions.with_api_key("<API-KEY>", "<API-KEY-ID>")
    return await ViamClient.create_from_dial_options(dial_options)

async def main():
    robot = await connect()

    # Can be used with any resource, using the motion service as an example
    motion = MotionClient.from_robot(robot=robot, name="builtin")

    robot.close()
Parameters:
  • robot (RobotClient) – The robot

  • name (str) – The name of the service

Returns:

The service, if it exists on the robot

Return type:

Self

abstract async do_command(command: Mapping[str, viam.utils.ValueTypes], *, timeout: float | None = None, **kwargs) Mapping[str, viam.utils.ValueTypes]

Send/receive arbitrary commands.

motion = MotionClient.from_robot(robot, "builtin")

my_command = {
  "cmnd": "dosomething",
  "someparameter": 52
}

# Can be used with any resource, using the motion service as an example
await motion.do_command(command=my_command)
Parameters:

command (Dict[str, ValueTypes]) – The command to execute

Returns:

Result of the executed command

Return type:

Dict[str, ValueTypes]

classmethod get_resource_name(name: str) viam.proto.common.ResourceName

Get the ResourceName for this Resource with the given name

# Can be used with any resource, using an arm as an example
my_arm_name = my_arm.get_resource_name("my_arm")
Parameters:

name (str) – The name of the Resource

Returns:

The ResourceName of this Resource

Return type:

ResourceName

get_operation(kwargs: Mapping[str, Any]) viam.operations.Operation

Get the Operation associated with the currently running function.

When writing custom resources, you should get the Operation by calling this function and check to see if it’s cancelled. If the Operation is cancelled, then you can perform any necessary (terminating long running tasks, cleaning up connections, etc. ).

Parameters:

kwargs (Mapping[str, Any]) – The kwargs object containing the operation

Returns:

The operation associated with this function

Return type:

viam.operations.Operation

async close()

Safely shut down the resource and prevent further use.

Close must be idempotent. Later configuration may allow a resource to be “open” again. If a resource does not want or need a close function, it is assumed that the resource does not need to return errors when future non-Close methods are called.

await component.close()