Struct ExecutionAccelerators

pub struct ExecutionAccelerators {
    pub gpu_execution_accelerator: Vec<Accelerator>,
    pub cpu_execution_accelerator: Vec<Accelerator>,
}

@@ @@ .. cpp:var:: message ExecutionAccelerators @@ @@ Specify the preferred execution accelerators to be used to execute @@ the model. Currently only recognized by ONNX Runtime backend and @@ TensorFlow backend. @@ @@ For ONNX Runtime backend, it will deploy the model with the execution @@ accelerators by priority, the priority is determined based on the @@ order that they are set, i.e. the provider at the front has highest @@ priority. Overall, the priority will be in the following order: @@ <gpu_execution_accelerator> (if instance is on GPU) @@ CUDA Execution Provider (if instance is on GPU) @@ <cpu_execution_accelerator> @@ Default CPU Execution Provider @@

Fields

gpu_execution_accelerator: Vec<Accelerator>

@@ .. cpp:var:: Accelerator gpu_execution_accelerator (repeated) @@ @@ The preferred execution provider to be used if the model instance @@ is deployed on GPU. @@ @@ For ONNX Runtime backend, possible value is “tensorrt” as name, @@ and no parameters are required. @@ @@ For TensorFlow backend, possible values are “tensorrt”, @@ “auto_mixed_precision”, “gpu_io”. @@ @@ For “tensorrt”, the following parameters can be specified: @@ “precision_mode”: The precision used for optimization. @@ Allowed values are “FP32” and “FP16”. Default value is “FP32”. @@ @@ “max_cached_engines”: The maximum number of cached TensorRT @@ engines in dynamic TensorRT ops. Default value is 100. @@ @@ “minimum_segment_size”: The smallest model subgraph that will @@ be considered for optimization by TensorRT. Default value is 3. @@ @@ “max_workspace_size_bytes”: The maximum GPU memory the model @@ can use temporarily during execution. Default value is 1GB. @@ @@ For “auto_mixed_precision”, no parameters are required. If set, @@ the model will try to use FP16 for better performance. @@ This optimization can not be set with “tensorrt”. @@ @@ For “gpu_io”, no parameters are required. If set, the model will @@ be executed using TensorFlow Callable API to set input and output @@ tensors in GPU memory if possible, which can reduce data transfer @@ overhead if the model is used in ensemble. However, the Callable @@ object will be created on model creation and it will request all @@ outputs for every model execution, which may impact the @@ performance if a request does not require all outputs. This @@ optimization will only take affect if the model instance is @@ created with KIND_GPU. @@

cpu_execution_accelerator: Vec<Accelerator>

@@ .. cpp:var:: Accelerator cpu_execution_accelerator (repeated) @@ @@ The preferred execution provider to be used if the model instance @@ is deployed on CPU. @@ @@ For ONNX Runtime backend, possible value is “openvino” as name, @@ and no parameters are required. @@

Trait Implementations

impl Clone for ExecutionAccelerators

fn clone(&self) -> ExecutionAccelerators

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for ExecutionAccelerators

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for ExecutionAccelerators

fn default() -> Self

Returns the “default value” for a type.

impl Message for ExecutionAccelerators

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited<B>(&self, buf: &mut B) -> Result<(), EncodeError>

where B: BufMut, Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited<B>(buf: B) -> Result<Self, DecodeError>

where B: Buf, Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited<B>(&mut self, buf: B) -> Result<(), DecodeError>

where B: Buf, Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for ExecutionAccelerators

fn eq(&self, other: &ExecutionAccelerators) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for ExecutionAccelerators