tf.experimental.tensorrt.ConversionParams

Parameters that are used for TF-TRT conversion.

max_workspace_size_bytes the maximum GPU temporary memory that the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize().
precision_mode one of the strings in TrtPrecisionMode.supported_precision_modes().
minimum_segment_size the minimum number of nodes required for a subgraph to be replaced by TRTEngineOp.
maximum_cached_engines max number of cached TRT engines for dynamic TRT ops. Created TRT engines for a dynamic dimension are cached. If the number of cached engines is already at max but none of them supports the input shapes, the TRTEngineOp will fall back to run the original TF subgraph that corresponds to the TRTEngineOp.
use_calibration this argument is ignored if precision_mode is not INT8. If set to True, a calibration graph will be created to calibrate the missing ranges. The calibration graph must be converted to an inference graph by running calibration with calibrate(). If set to False, quantization nodes will be expected for every tensor in the graph (excluding those which will be fused). If a range is missing, an error will occur. Please note that accuracy may be negatively affected if there is a mismatch between which tensors TRT quantizes and which tensors were trained with fake quantization.
allow_build_at_runtime whether to allow building TensorRT engines during runtime if no prebuilt TensorRT engine can be found that can handle the given inputs during runtime, then a new TensorRT engine is built at runtime if allow_build_at_runtime=True, and otherwise native TF is used.

max_workspace_size_bytes A namedtuple alias for field number 0
precision_mode A namedtuple alias for field number 1
minimum_segment_size A namedtuple alias for field number 2
maximum_cached_engines A namedtuple alias for field number 3
use_calibration A namedtuple alias for field number 4
allow_build_at_runtime A namedtuple alias for field number 5