viam.services.mlmodel.client

Classes

InferRequest

Abstract base class for protocol messages.

InferResponse

Abstract base class for protocol messages.

MetadataRequest

Abstract base class for protocol messages.

MetadataResponse

Abstract base class for protocol messages.

MLModelServiceStub

ReconfigurableResourceRPCClientBase

A base RPC client that can reset its channel.

Metadata

Abstract base class for protocol messages.

MLModel

MLModel represents a Machine Learning Model service.

MLModelClient

MLModel represents a Machine Learning Model service.

Functions

flat_tensors_to_ndarrays(→ Dict[str, numpy.typing.NDArray])

ndarrays_to_flat_tensors(...)

Module Contents

class viam.services.mlmodel.client.InferRequest(*, name: str = ..., input_tensors: global___FlatTensors | None = ..., extra: google.protobuf.struct_pb2.Struct | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

name: str

name of the model service

property input_tensors: global___FlatTensors

the input data is provided as set of named flat tensors

property extra: google.protobuf.struct_pb2.Struct

Additional arguments to the method

HasField(field_name: Literal['extra', b'extra', 'input_tensors', b'input_tensors']) bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:

field_name (str) – The name of the field to check for presence.

Returns:

Whether a value has been set for the named field.

Return type:

bool

Raises:

ValueError – if the field_name is not a member of this message.

class viam.services.mlmodel.client.InferResponse(*, output_tensors: global___FlatTensors | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

property output_tensors: global___FlatTensors

the output data is provided as a set of named flat tensors

HasField(field_name: Literal['output_tensors', b'output_tensors']) bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:

field_name (str) – The name of the field to check for presence.

Returns:

Whether a value has been set for the named field.

Return type:

bool

Raises:

ValueError – if the field_name is not a member of this message.

class viam.services.mlmodel.client.MetadataRequest(*, name: str = ..., extra: google.protobuf.struct_pb2.Struct | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

name: str

name of the model service

property extra: google.protobuf.struct_pb2.Struct

Additional arguments to the method

HasField(field_name: Literal['extra', b'extra']) bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:

field_name (str) – The name of the field to check for presence.

Returns:

Whether a value has been set for the named field.

Return type:

bool

Raises:

ValueError – if the field_name is not a member of this message.

class viam.services.mlmodel.client.MetadataResponse(*, metadata: global___Metadata | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

property metadata: global___Metadata

this is the metadata associated with the ML model

HasField(field_name: Literal['metadata', b'metadata']) bool

Checks if a certain field is set for the message.

For a oneof group, checks if any field inside is set. Note that if the field_name is not defined in the message descriptor, ValueError will be raised.

Parameters:

field_name (str) – The name of the field to check for presence.

Returns:

Whether a value has been set for the named field.

Return type:

bool

Raises:

ValueError – if the field_name is not a member of this message.

class viam.services.mlmodel.client.MLModelServiceStub(channel: grpclib.client.Channel)[source]
class viam.services.mlmodel.client.ReconfigurableResourceRPCClientBase[source]

Bases: ResourceRPCClientBase

A base RPC client that can reset its channel.

Useful if connection is lost and then regained.

reset_channel(channel: grpclib.client.Channel)[source]

Called when the RPC channel was reset. Passes in the new channel.

Parameters:

channel (Channel) – The new RPC Channel

viam.services.mlmodel.client.flat_tensors_to_ndarrays(flat_tensors: viam.proto.service.mlmodel.FlatTensors) Dict[str, numpy.typing.NDArray][source]
viam.services.mlmodel.client.ndarrays_to_flat_tensors(ndarrays: Dict[str, numpy.typing.NDArray]) viam.proto.service.mlmodel.FlatTensors[source]
class viam.services.mlmodel.client.Metadata(*, name: str = ..., type: str = ..., description: str = ..., input_info: collections.abc.Iterable[global___TensorInfo] | None = ..., output_info: collections.abc.Iterable[global___TensorInfo] | None = ...)

Bases: google.protobuf.message.Message

Abstract base class for protocol messages.

Protocol message classes are almost always generated by the protocol compiler. These generated types subclass Message and implement the methods shown below.

name: str

name of the model

type: str

type of model for example object_detector, text_classifier

description: str

description of the model

property input_info: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]

the necessary input arrays/tensors for an inference, order matters

property output_info: google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___TensorInfo]

the output arrays/tensors of the model, order matters

class viam.services.mlmodel.client.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 infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: float | None) Dict[str, numpy.typing.NDArray][source]
Async:

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

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

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()
class viam.services.mlmodel.client.MLModelClient(name: str, channel: grpclib.client.Channel)[source]

Bases: viam.services.mlmodel.mlmodel.MLModel, viam.resource.rpc_client_base.ReconfigurableResourceRPCClientBase

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.

async infer(input_tensors: Dict[str, numpy.typing.NDArray], *, timeout: float | None = 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]

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

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()