tf.experimental.tensorrt.ConversionParams

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Parameters that are used for TF-TRT conversion.

tf.experimental.tensorrt.ConversionParams(
    max_workspace_size_bytes=DEFAULT_TRT_MAX_WORKSPACE_SIZE_BYTES,
    precision_mode=TrtPrecisionMode.FP32, minimum_segment_size=3,
    maximum_cached_engines=1, use_calibration=True, allow_build_at_runtime=True
)

Fields:

• max_workspace_size_bytes: the maximum GPU temporary memory which the TRT engine can use at execution time. This corresponds to the 'workspaceSize' parameter of nvinfer1::IBuilder::setMaxWorkspaceSize().
• precision_mode: one 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. This is the maximum number of engines that can be 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 build TensorRT engines during runtime. If no TensorRT engine can be found in cache 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.