morpheus.stages.inference.inference_stage.InferenceStage#

class InferenceStage(c, thread_count=None)[source]#

Bases: ControlMessageStage

This class serves as the base for any inference stage. Inference stages operate differently than other stages due to the fact that they operate in a separate thread and have their own batch size which is separate from the pipeline batch size. Processing the inference work in a separate thread is necessary to support inference types that may require exclusive use of a single thread (i.e., TensorRT) without blocking the main thread.

Changing batch sizes for the inference stage requires breaking messages into smaller slices, running inference on the smaller slices, then recombining the inference output into the original batch size. This inference base class handles breaking and recombining batches and queing the inference work to be processed on another thread.

Inference stages that derive from this class need to implement the _get_inference_worker method which returns an implementation of the InferenceWorker class. Your InferenceWorker class must implement the process and calc_output_dims methods. The process methods is where you provide implementation details on how to perform inference with the ControlMessage batch. The worker uses the calc_output_dims to calculate the output dimensions of the pipeline batch that inference batch results are appended to.

To add a C++ implementation for processing inference requests, you must implement the _get_cpp_inference_node method and implement supports_cpp_node in your worker to return True. Your pipeline can then use your C++ implementation by setting use_cpp to True in your pipeline configuration. See developer documentation for more details.

Parameters:

cmorpheus.config.Config: Pipeline configuration instance.
thread_countint, optional: Number of threads to use for inference. If not provided, the num_threads attribute of the Config object will be used.

Attributes:

has_multi_input_ports: Indicates if this stage has multiple input ports.
has_multi_output_ports: Indicates if this stage has multiple output ports.
input_ports: Input ports to this stage.
is_built: Indicates if this stage has been built.
is_pre_built: Indicates if this stage has been built.
name: The name of the stage.
output_ports: Output ports from this stage.
unique_name: Unique name of stage.

Methods

`accepted_types`()	Accepted input types to this stage.
`build`(builder[, do_propagate])	Build this stage.
`can_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`can_pre_build`([check_ports])	Determines if all inputs have been built allowing this node to be built.
`compute_schema`(schema)	Compute the schema for this stage based on the incoming schema from upstream stages.
`get_all_input_stages`()	Get all input stages to this stage.
`get_all_inputs`()	Get all input senders to this stage.
`get_all_output_stages`()	Get all output stages from this stage.
`get_all_outputs`()	Get all output receivers from this stage.
`get_needed_columns`()	Stages which need to have columns inserted into the dataframe, should populate the `self._needed_columns` dictionary with mapping of column names to `morpheus.common.TypeId`.
`join`()	On all inference worker threads, this function applies join.
`start_async`()	This function is called along with on_start during stage initialization.
`stop`()	Stops the inference workers and closes the inference queue.
`supported_execution_modes`()	Returns a tuple of supported execution modes of this stage.
`supports_cpp_node`()	Specifies whether this Stage is capable of creating C++ nodes.

_build(builder, input_nodes)[source]#

This function is responsible for constructing this stage’s internal mrc.SegmentObject object. The input of this function contains the returned value from the upstream stage.

The input values are the mrc.Builder for this stage and a list of parent nodes.

Parameters:

buildermrc.Builder: mrc.Builder object for the pipeline. This should be used to construct/attach the internal mrc.SegmentObject.
input_nodeslist[mrc.SegmentObject]: List containing the input mrc.SegmentObject objects.

Returns:

list[mrc.SegmentObject]: List of tuples containing the output mrc.SegmentObject object from this stage.

_get_inference_worker(inf_queue)[source]#

Returns the main inference worker which manages requests possibly in another thread depending on which mode the pipeline is currently operating in.

Parameters:

inf_queuemorpheus.utils.producer_consumer_queue.ProducerConsumerQueue: Inference request queue.

Returns:

InferenceWorker: Inference worker implementation for stage.

accepted_types()[source]#

Accepted input types to this stage.

Returns:

tuple: Tuple of input types.

build(builder, do_propagate=True)[source]#

Build this stage.

Parameters:

buildermrc.Builder: MRC segment for this stage.
do_propagatebool, optional: Whether to propagate to build output stages, by default True.

can_build(check_ports=False)[source]#

Determines if all inputs have been built allowing this node to be built.

Parameters:

check_portsbool, optional: Check if we can build based on the input ports, by default False.

Returns:

bool: True if we can build, False otherwise.

can_pre_build(check_ports=False)[source]#

Determines if all inputs have been built allowing this node to be built.

Parameters:

check_portsbool, optional: Check if we can build based on the input ports, by default False.

Returns:

bool: True if we can build, False otherwise.

compute_schema(schema)[source]#

Compute the schema for this stage based on the incoming schema from upstream stages.

Incoming schema and type information from upstream stages is available via the schema.input_schemas and schema.input_types properties.

Derived classes need to override this method, can set the output type(s) on schema by calling set_type for all output ports. For example a simple pass-thru stage might perform the following:

>>> for (port_idx, port_schema) in enumerate(schema.input_schemas):
...     schema.output_schemas[port_idx].set_type(port_schema.get_type())
>>>

If the port types in upstream_schema are incompatible the stage should raise a RuntimeError.

get_all_input_stages()[source]#

Get all input stages to this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All input stages.

get_all_inputs()[source]#

Get all input senders to this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: All input senders.

get_all_output_stages()[source]#

Get all output stages from this stage.

Returns:

list[morpheus.pipeline.pipeline.StageBase]: All output stages.

get_all_outputs()[source]#

Get all output receivers from this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: All output receivers.

get_needed_columns()[source]#: Stages which need to have columns inserted into the dataframe, should populate the self._needed_columns dictionary with mapping of column names to morpheus.common.TypeId. This will ensure that the columns are allocated and populated with null values.

property has_multi_input_ports: bool#

Indicates if this stage has multiple input ports.

Returns:

bool: True if stage has multiple input ports, False otherwise.

property has_multi_output_ports: bool#

Indicates if this stage has multiple output ports.

Returns:

bool: True if stage has multiple output ports, False otherwise.

property input_ports: list[Receiver]#

Input ports to this stage.

Returns:

list[morpheus.pipeline.pipeline.Receiver]: Input ports to this stage.

property is_built: bool#

Indicates if this stage has been built.

Returns:

bool: True if stage is built, False otherwise.

property is_pre_built: bool#

Indicates if this stage has been built.

Returns:

bool: True if stage is built, False otherwise.

async join()[source]#: On all inference worker threads, this function applies join.

property name: str#

The name of the stage. Used in logging. Each derived class should override this property with a unique name.

Returns:

str: Name of a stage.

property output_ports: list[Sender]#

Output ports from this stage.

Returns:

list[morpheus.pipeline.pipeline.Sender]: Output ports from this stage.

async start_async()[source]#: This function is called along with on_start during stage initialization. Allows stages to utilize the asyncio loop if needed.

stop()[source]#: Stops the inference workers and closes the inference queue.

supported_execution_modes()[source]#

Returns a tuple of supported execution modes of this stage. By default this returns (ExecutionMode.GPU,). Subclasses can override this method to specify different execution modes.

For most stages the values will be static, and this can be accomplished by making use of either the CpuOnlyMixin or GpuAndCpuMixin mixins.

However, complex stages may choose to make this decision at runtime, in which case this method should be overridden. directly within the stage class.

supports_cpp_node()[source]#

Specifies whether this Stage is capable of creating C++ nodes. During the build phase, this value will be combined with CppConfig.get_should_use_cpp() to determine whether or not a C++ node is created. This is an instance method to allow runtime decisions and derived classes to override base implementations.

property unique_name: str#

Unique name of stage. Generated by appending stage id to stage name.

Returns:

str: Unique name of stage.