apache_beam.ml.inference.tensorrt_inference module

class apache_beam.ml.inference.tensorrt_inference.TensorRTEngine(engine: <sphinx.ext.autodoc.importer._MockObject object at 0x7fd7301bddc0>)

Bases: object

Implementation of the TensorRTEngine class which handles allocations associated with TensorRT engine.

Example Usage:

TensorRTEngine(engine)

Parameters:engine – trt.ICudaEngine object that contains TensorRT engine

get_engine_attrs()

Returns TensorRT engine attributes.

class apache_beam.ml.inference.tensorrt_inference.TensorRTEngineHandlerNumPy(min_batch_size: int, max_batch_size: int, *, inference_fn: Callable[[Sequence[numpy.ndarray], apache_beam.ml.inference.tensorrt_inference.TensorRTEngine, Optional[Dict[str, Any]]], Iterable[apache_beam.ml.inference.base.PredictionResult]] = <function _default_tensorRT_inference_fn>, large_model: bool = False, **kwargs)

Bases: apache_beam.ml.inference.base.ModelHandler

Implementation of the ModelHandler interface for TensorRT.

Example Usage:

pcoll | RunInference(
    TensorRTEngineHandlerNumPy(
      min_batch_size=1,
      max_batch_size=1,
      engine_path="my_uri"))

NOTE: This API and its implementation are under development and do not provide backward compatibility guarantees.

Parameters:

• min_batch_size – minimum accepted batch size.
• max_batch_size – maximum accepted batch size.
• inference_fn – the inference function to use on RunInference calls. default: _default_tensorRT_inference_fn
• large_model – set to true if your model is large enough to run into memory pressure if you load multiple copies. Given a model that consumes N memory and a machine with W cores and M memory, you should set this to True if N*W > M.
• kwargs – Additional arguments like ‘engine_path’ and ‘onnx_path’ are currently supported. ‘env_vars’ can be used to set environment variables before loading the model.

See https://docs.nvidia.com/deeplearning/tensorrt/api/python_api/ for details

batch_elements_kwargs()

Sets min_batch_size and max_batch_size of a TensorRT engine.

load_model() → apache_beam.ml.inference.tensorrt_inference.TensorRTEngine

Loads and initializes a TensorRT engine for processing.

load_onnx() → Tuple[<sphinx.ext.autodoc.importer._MockObject object at 0x7fd731b325b0>, <sphinx.ext.autodoc.importer._MockObject object at 0x7fd731b32940>]

Loads and parses an onnx model for processing.

build_engine(network: <sphinx.ext.autodoc.importer._MockObject object at 0x7fd7301c7340>, builder: <sphinx.ext.autodoc.importer._MockObject object at 0x7fd7301c73d0>) → apache_beam.ml.inference.tensorrt_inference.TensorRTEngine

Build an engine according to parsed/created network.

run_inference(batch: Sequence[numpy.ndarray], engine: apache_beam.ml.inference.tensorrt_inference.TensorRTEngine, inference_args: Optional[Dict[str, Any]] = None) → Iterable[apache_beam.ml.inference.base.PredictionResult]

Runs inferences on a batch of Tensors and returns an Iterable of TensorRT Predictions.

Parameters:

• batch – A np.ndarray or a np.ndarray that represents a concatenation of multiple arrays as a batch.
• engine – A TensorRT engine.
• inference_args – Any additional arguments for an inference that are not applicable to TensorRT.

Returns:

An Iterable of type PredictionResult.

get_num_bytes(batch: Sequence[numpy.ndarray]) → int

Returns:The number of bytes of data for a batch of Tensors.

get_metrics_namespace() → str

Returns a namespace for metrics collected by the RunInference transform.

share_model_across_processes() → bool