morpheus.messages.multi_inference_message.MultiInferenceMessage

class MultiInferenceMessage(*args, **kwargs)[source]

Bases: morpheus.messages.multi_message.MultiMessage

This is a container class that holds the TensorMemory container and the metadata of the data contained within it. Builds on top of the MultiMessage class to add additional data for inferencing.

This class requires two separate memory blocks for a batch. One for the message metadata (i.e., start time, IP address, etc.) and another for the raw inference inputs (i.e., input_ids, seq_ids). Since there can be more inference input requests than messages (This happens when some messages get broken into multiple inference requests) this class stores two different offset and count values. mess_offset and mess_count refer to the offset and count in the message metadata batch and offset and count index into the inference batch data.

Parameters
memory<bsp-code-inline code="TensorMemory">TensorMemory</bsp-code-inline>

Inference memory.

offsetint

Message offset in inference memory instance.

countint

Message count in inference memory instance.

Attributes
id

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df as list.

id_col

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df.

inputs

Get inputs stored in the TensorMemory container.

timestamp

Returns timestamp column values from morpheus.messages.MessageMeta.df as list.

Methods

copy_meta_ranges(ranges[, mask])

Perform a copy of the underlying dataframe for the given ranges of rows.

copy_ranges(ranges[, num_selected_rows])

Perform a copy of the current message instance for the given ranges of rows.

get_input(name)

Get input stored in the TensorMemory container.

get_meta([columns])

Return column values from morpheus.pipeline.messages.MessageMeta.df.

get_meta_list([col_name])

Return a column values from morpheus.messages.MessageMeta.df as a list.

get_slice(start, stop)

Returns sliced batches based on offsets supplied.

set_meta(columns, value)

Set column values to morpheus.pipelines.messages.MessageMeta.df.

copy_meta_ranges(ranges, mask=None)[source]

Perform a copy of the underlying dataframe for the given ranges of rows.

Parameters
rangestyping.List[typing.Tuple[int, int]]

Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]

masktyping.Union[None, cupy.ndarray, numpy.ndarray]

Optionally specify rows as a cupy array (when using cudf Dataframes) or a numpy array (when using pandas Dataframes) of booleans. When not-None ranges will be ignored. This is useful as an optimization as this avoids needing to generate the mask on it’s own.

Returns
Dataframe
copy_ranges(ranges, num_selected_rows=None)[source]

Perform a copy of the current message instance for the given ranges of rows.

Parameters
rangestyping.List[typing.Tuple[int, int]]

Rows to include in the copy in the form of [(`start_row, stop_row),…]` The stop_row isn’t included. For example to copy rows 1-2 & 5-7 ranges=[(1, 3), (5, 8)]

num_selected_rowstyping.Union[None, int]

Optional specify the number of rows selected by ranges, otherwise this is computed by the result.

Returns
MultiMessage
get_input(name)[source]

Get input stored in the TensorMemory container.

Parameters
namestr

Input key name.

Returns
cupy.ndarray

Inference input.

get_meta(columns=None)[source]

Return column values from morpheus.pipeline.messages.MessageMeta.df.

Parameters
columnstyping.Union[None, str, typing.List[str]]

Input column names. Returns all columns if None is specified. When a string is passed, a Series is returned. Otherwise a Dataframe is returned.

Returns
Series or Dataframe

Column values from the dataframe.

get_meta_list(col_name=None)[source]

Return a column values from morpheus.messages.MessageMeta.df as a list.

Parameters
col_namestr

Column name in the dataframe.

Returns
List[str]

Column values from the dataframe.

get_slice(start, stop)[source]

Returns sliced batches based on offsets supplied. Automatically calculates the correct mess_offset and mess_count.

Parameters
startint

Start offset address.

stopint

Stop offset address.

Returns
MultiInferenceMessage

A new MultiInferenceMessage with sliced offset and count.

property id: List[int]

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df as list.

Returns
List[int]

ID column values from the dataframe as list.

property id_col

Returns ID column values from morpheus.pipeline.messages.MessageMeta.df.

Returns
pandas.Series

ID column values from the dataframe.

property inputs

Get inputs stored in the TensorMemory container.

Returns
cupy.ndarray

Inference inputs.

set_meta(columns, value)[source]

Set column values to morpheus.pipelines.messages.MessageMeta.df.

Parameters
columnstyping.Union[None, str, typing.List[str]]

Input column names. Sets the value for the corresponding column names. If None is specified, all columns will be used. If the column does not exist, a new one will be created.

valueAny

Value to apply to the specified columns. If a single value is passed, it will be broadcast to all rows. If a Series or Dataframe is passed, rows will be matched by index.

property timestamp: List[int]

Returns timestamp column values from morpheus.messages.MessageMeta.df as list.

Returns
List[int]

Timestamp column values from the dataframe as list.

© Copyright 2023, NVIDIA. Last updated on Feb 3, 2023.