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model_config.proto
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model_config.proto
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// Copyright (c) 2018-2020, NVIDIA CORPORATION. All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// * Neither the name of NVIDIA CORPORATION nor the names of its
// contributors may be used to endorse or promote products derived
// from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// Copyright (c) 2018, TensorFlow Authors. All rights reserved.
syntax = "proto3";
package inference;
//@@.. cpp:namespace:: inference
//@@
//@@.. cpp:enum:: DataType
//@@
//@@ Data types supported for input and output tensors.
//@@
enum DataType {
//@@ .. cpp:enumerator:: DataType::INVALID = 0
TYPE_INVALID = 0;
//@@ .. cpp:enumerator:: DataType::BOOL = 1
TYPE_BOOL = 1;
//@@ .. cpp:enumerator:: DataType::UINT8 = 2
TYPE_UINT8 = 2;
//@@ .. cpp:enumerator:: DataType::UINT16 = 3
TYPE_UINT16 = 3;
//@@ .. cpp:enumerator:: DataType::UINT32 = 4
TYPE_UINT32 = 4;
//@@ .. cpp:enumerator:: DataType::UINT64 = 5
TYPE_UINT64 = 5;
//@@ .. cpp:enumerator:: DataType::INT8 = 6
TYPE_INT8 = 6;
//@@ .. cpp:enumerator:: DataType::INT16 = 7
TYPE_INT16 = 7;
//@@ .. cpp:enumerator:: DataType::INT32 = 8
TYPE_INT32 = 8;
//@@ .. cpp:enumerator:: DataType::INT64 = 9
TYPE_INT64 = 9;
//@@ .. cpp:enumerator:: DataType::FP16 = 10
TYPE_FP16 = 10;
//@@ .. cpp:enumerator:: DataType::FP32 = 11
TYPE_FP32 = 11;
//@@ .. cpp:enumerator:: DataType::FP64 = 12
TYPE_FP64 = 12;
//@@ .. cpp:enumerator:: DataType::STRING = 13
TYPE_STRING = 13;
}
//@@
//@@ .. cpp:var:: message ModelRateLimiter
//@@
//@@ The specifications required by the rate limiter to properly
//@@ schedule the inference requests across the different models
//@@ and their instances.
//@@
message ModelRateLimiter
{
//@@ .. cpp:var:: message Resource
//@@
//@@ The resource property.
//@@
message Resource
{
//@@ .. cpp:var:: string name
//@@
//@@ The name associated with the resource.
//@@
string name = 1;
//@@ .. cpp:var:: bool global
//@@
//@@ Whether or not the resource is global. If true then the resource
//@@ is assumed to be shared among the devices otherwise specified
//@@ count of the resource is assumed for each device associated
//@@ with the instance.
//@@
bool global = 2;
//@@ .. cpp:var:: uint32 count
//@@
//@@ The number of resources required for the execution of the model
//@@ instance.
//@@
uint32 count = 3;
}
//@@ .. cpp:var:: Resource resources (repeated)
//@@
//@@ The resources required to execute the request on a model instance.
//@@ Resources are just names with a corresponding count. The execution
//@@ of the instance will be blocked until the specificied resources are
//@@ available. By default an instance uses no rate-limiter resources.
//@@
repeated Resource resources = 1;
//@@ .. cpp:var:: uint32 priority
//@@
//@@ The weighting value to be used for prioritizing across instances.
//@@ An instance with priority 2 will be given 1/2 the number of
//@@ scheduling chances as an instance_group with priority 1. The
//@@ default priority is 1.
//@@
uint32 priority = 2;
}
//@@
//@@.. cpp:var:: message ModelInstanceGroup
//@@
//@@ A group of one or more instances of a model and resources made
//@@ available for those instances.
//@@
message ModelInstanceGroup
{
//@@
//@@ .. cpp:enum:: Kind
//@@
//@@ Kind of this instance group.
//@@
enum Kind {
//@@ .. cpp:enumerator:: Kind::KIND_AUTO = 0
//@@
//@@ This instance group represents instances that can run on either
//@@ CPU or GPU. If all GPUs listed in 'gpus' are available then
//@@ instances will be created on GPU(s), otherwise instances will
//@@ be created on CPU.
//@@
KIND_AUTO = 0;
//@@ .. cpp:enumerator:: Kind::KIND_GPU = 1
//@@
//@@ This instance group represents instances that must run on the
//@@ GPU.
//@@
KIND_GPU = 1;
//@@ .. cpp:enumerator:: Kind::KIND_CPU = 2
//@@
//@@ This instance group represents instances that must run on the
//@@ CPU.
//@@
KIND_CPU = 2;
//@@ .. cpp:enumerator:: Kind::KIND_MODEL = 3
//@@
//@@ This instance group represents instances that should run on the
//@@ CPU and/or GPU(s) as specified by the model or backend itself.
//@@ The inference server will not override the model/backend
//@@ settings.
//@@ Currently, this option is supported only for Tensorflow models.
//@@
KIND_MODEL = 3;
}
//@@ .. cpp:var:: string name
//@@
//@@ Optional name of this group of instances. If not specified the
//@@ name will be formed as <model name>_<group number>. The name of
//@@ individual instances will be further formed by a unique instance
//@@ number and GPU index:
//@@
string name = 1;
//@@ .. cpp:var:: Kind kind
//@@
//@@ The kind of this instance group. Default is KIND_AUTO. If
//@@ KIND_AUTO or KIND_GPU then both 'count' and 'gpu' are valid and
//@@ may be specified. If KIND_CPU or KIND_MODEL only 'count' is valid
//@@ and 'gpu' cannot be specified.
//@@
Kind kind = 4;
//@@ .. cpp:var:: int32 count
//@@
//@@ For a group assigned to GPU, the number of instances created for
//@@ each GPU listed in 'gpus'. For a group assigned to CPU the number
//@@ of instances created. Default is 1.
int32 count = 2;
//@@ .. cpp:var:: ModelRateLimiter rate_limiter
//@@
//@@ The rate limiter specific settings to be associated with this
//@@ instance group. Optional, if not specified no rate limiting
//@@ will be applied to this instance group.
//@@
ModelRateLimiter rate_limiter = 6;
//@@ .. cpp:var:: int32 gpus (repeated)
//@@
//@@ GPU(s) where instances should be available. For each GPU listed,
//@@ 'count' instances of the model will be available. Setting 'gpus'
//@@ to empty (or not specifying at all) is eqivalent to listing all
//@@ available GPUs.
//@@
repeated int32 gpus = 3;
//@@ .. cpp:var:: string profile (repeated)
//@@
//@@ For TensorRT models containing multiple optimization profile, this
//@@ parameter specifies a set of optimization profiles available to this
//@@ instance group. The inference server will choose the optimal profile
//@@ based on the shapes of the input tensors. This field should lie
//@@ between 0 and <TotalNumberOfOptimizationProfilesInPlanModel> - 1
//@@ and be specified only for TensorRT backend, otherwise an error will
//@@ be generated. If not specified, the server will select the first
//@@ optimization profile by default.
//@@
repeated string profile = 5;
}
//@@
//@@.. cpp:var:: message ModelTensorReshape
//@@
//@@ Reshape specification for input and output tensors.
//@@
message ModelTensorReshape
{
//@@ .. cpp:var:: int64 shape (repeated)
//@@
//@@ The shape to use for reshaping.
//@@
repeated int64 shape = 1;
}
//@@
//@@.. cpp:var:: message ModelInput
//@@
//@@ An input required by the model.
//@@
message ModelInput
{
//@@
//@@ .. cpp:enum:: Format
//@@
//@@ The format for the input.
//@@
enum Format {
//@@ .. cpp:enumerator:: Format::FORMAT_NONE = 0
//@@
//@@ The input has no specific format. This is the default.
//@@
FORMAT_NONE = 0;
//@@ .. cpp:enumerator:: Format::FORMAT_NHWC = 1
//@@
//@@ HWC image format. Tensors with this format require 3 dimensions
//@@ if the model does not support batching (max_batch_size = 0) or 4
//@@ dimensions if the model does support batching (max_batch_size
//@@ >= 1). In either case the 'dims' below should only specify the
//@@ 3 non-batch dimensions (i.e. HWC or CHW).
//@@
FORMAT_NHWC = 1;
//@@ .. cpp:enumerator:: Format::FORMAT_NCHW = 2
//@@
//@@ CHW image format. Tensors with this format require 3 dimensions
//@@ if the model does not support batching (max_batch_size = 0) or 4
//@@ dimensions if the model does support batching (max_batch_size
//@@ >= 1). In either case the 'dims' below should only specify the
//@@ 3 non-batch dimensions (i.e. HWC or CHW).
//@@
FORMAT_NCHW = 2;
}
//@@ .. cpp:var:: string name
//@@
//@@ The name of the input.
//@@
string name = 1;
//@@ .. cpp:var:: DataType data_type
//@@
//@@ The data-type of the input.
//@@
DataType data_type = 2;
//@@ .. cpp:var:: Format format
//@@
//@@ The format of the input. Optional.
//@@
Format format = 3;
//@@ .. cpp:var:: int64 dims (repeated)
//@@
//@@ The dimensions/shape of the input tensor that must be provided
//@@ when invoking the inference API for this model.
//@@
repeated int64 dims = 4;
//@@ .. cpp:var:: ModelTensorReshape reshape
//@@
//@@ The shape expected for this input by the backend. The input will
//@@ be reshaped to this before being presented to the backend. The
//@@ reshape must have the same number of elements as the input shape
//@@ specified by 'dims'. Optional.
//@@
ModelTensorReshape reshape = 5;
//@@ .. cpp:var:: bool is_shape_tensor
//@@
//@@ Whether or not the input is a shape tensor to the model. This field
//@@ is currently supported only for the TensorRT model. An error will be
//@@ generated if this specification does not comply with underlying
//@@ model.
//@@
bool is_shape_tensor = 6;
//@@ .. cpp:var:: bool allow_ragged_batch
//@@
//@@ Whether or not the input is allowed to be "ragged" in a dynamically
//@@ created batch. Default is false indicating that two requests will
//@@ only be batched if this tensor has the same shape in both requests.
//@@ True indicates that two requests can be batched even if this tensor
//@@ has a different shape in each request. A true value is currently
//@@ supported only for custom models.
//@@
bool allow_ragged_batch = 7;
}
//@@
//@@.. cpp:var:: message ModelOutput
//@@
//@@ An output produced by the model.
//@@
message ModelOutput
{
//@@ .. cpp:var:: string name
//@@
//@@ The name of the output.
//@@
string name = 1;
//@@ .. cpp:var:: DataType data_type
//@@
//@@ The data-type of the output.
//@@
DataType data_type = 2;
//@@ .. cpp:var:: int64 dims (repeated)
//@@
//@@ The dimensions/shape of the output tensor.
//@@
repeated int64 dims = 3;
//@@ .. cpp:var:: ModelTensorReshape reshape
//@@
//@@ The shape produced for this output by the backend. The output will
//@@ be reshaped from this to the shape specifed in 'dims' before being
//@@ returned in the inference response. The reshape must have the same
//@@ number of elements as the output shape specified by 'dims'. Optional.
//@@
ModelTensorReshape reshape = 5;
//@@ .. cpp:var:: string label_filename
//@@
//@@ The label file associated with this output. Should be specified only
//@@ for outputs that represent classifications. Optional.
//@@
string label_filename = 4;
//@@ .. cpp:var:: bool is_shape_tensor
//@@
//@@ Whether or not the output is a shape tensor to the model. This field
//@@ is currently supported only for the TensorRT model. An error will be
//@@ generated if this specification does not comply with underlying
//@@ model.
//@@
bool is_shape_tensor = 6;
}
//@@ .. cpp:var:: message BatchInput
//@@
//@@ A batch input is an additional input that must be added by
//@@ the backend based on all the requests in a batch.
//@@
message BatchInput
{
//@@
//@@ .. cpp:enum:: Kind
//@@
//@@ The kind of the batch input.
//@@
enum Kind {
//@@ .. cpp:enumerator:: Kind::BATCH_ELEMENT_COUNT = 0
//@@
//@@ The element count of the 'source_input' will be added as
//@@ input with shape [1].
//@@
BATCH_ELEMENT_COUNT = 0;
//@@ .. cpp:enumerator:: Kind::BATCH_ACCUMULATED_ELEMENT_COUNT = 1
//@@
//@@ The accumulated element count of the 'source_input' will be
//@@ added as input with shape [1]. For example, if there is a
//@@ batch of two request, each with 2 elements, an input of value
//@@ 2 will be added to the first request, and an input of value
//@@ 4 will be added to the second request.
//@@
BATCH_ACCUMULATED_ELEMENT_COUNT = 1;
//@@ .. cpp:enumerator::
//@@ Kind::BATCH_ACCUMULATED_ELEMENT_COUNT_WITH_ZERO = 2
//@@
//@@ The accumulated element count of the 'source_input' will be
//@@ added as input with shape [1], except for the first request
//@@ in the batch. For the first request in the batch, the input
//@@ will have shape [2] where the first element is value 0.
//@@
BATCH_ACCUMULATED_ELEMENT_COUNT_WITH_ZERO = 2;
//@@ .. cpp:enumerator:: Kind::BATCH_MAX_ELEMENT_COUNT_AS_SHAPE = 3
//@@
//@@ Among the requests in the batch, the max element count of the
//@@ 'source_input' will be added as input with shape
//@@ [max_element_count] for the first request in the batch.
//@@ For other requests, such input will be with shape [0].
//@@ The data of the tensor will be uninitialized.
//@@
BATCH_MAX_ELEMENT_COUNT_AS_SHAPE = 3;
}
//@@ .. cpp:var:: Kind kind
//@@
//@@ The kind of this batch input.
//@@
Kind kind = 1;
//@@ .. cpp:var:: string target_name (repeated)
//@@
//@@ The name of the model inputs that the backend will create
//@@ for this batch input.
//@@
repeated string target_name = 2;
//@@ .. cpp:var:: DataType data_type
//@@
//@@ The input's datatype. The data type can be TYPE_INT32 or
//@@ TYPE_FP32.
//@@
DataType data_type = 3;
//@@ .. cpp:var:: string source_input (repeated)
//@@
//@@ The backend derives the value for each batch input from one or
//@@ more other inputs. 'source_input' gives the names of those
//@@ inputs.
//@@
repeated string source_input = 4;
}
//@@.. cpp:var:: message BatchOutput
//@@
//@@ A batch output is an output produced by the model that must be handled
//@@ differently by the backend based on all the requests in a batch.
//@@
message BatchOutput
{
//@@
//@@ .. cpp:enum:: Kind
//@@
//@@ The kind of the batch output.
//@@
enum Kind {
//@@ .. cpp:enumerator:: Kind::BATCH_SCATTER_WITH_INPUT_SHAPE = 0
//@@
//@@ The output should be scattered according to the shape of
//@@ 'source_input'. The dynamic dimension of the output will
//@@ be set to the value of the same dimension in the input.
//@@
BATCH_SCATTER_WITH_INPUT_SHAPE = 0;
}
//@@ .. cpp:var:: string target_name (repeated)
//@@
//@@ The name of the outputs to be produced by this batch output
//@@ specification.
//@@
repeated string target_name = 1;
//@@ .. cpp:var:: Kind kind
//@@
//@@ The kind of this batch output.
//@@
Kind kind = 2;
//@@ .. cpp:var:: string source_input (repeated)
//@@
//@@ The backend derives each batch output from one or more inputs.
//@@ 'source_input' gives the names of those inputs.
//@@
repeated string source_input = 3;
}
//@@
//@@.. cpp:var:: message ModelVersionPolicy
//@@
//@@ Policy indicating which versions of a model should be made
//@@ available by the inference server.
//@@
message ModelVersionPolicy
{
//@@ .. cpp:var:: message Latest
//@@
//@@ Serve only the latest version(s) of a model. This is
//@@ the default policy.
//@@
message Latest
{
//@@ .. cpp:var:: uint32 num_versions
//@@
//@@ Serve only the 'num_versions' highest-numbered versions. T
//@@ The default value of 'num_versions' is 1, indicating that by
//@@ default only the single highest-number version of a
//@@ model will be served.
//@@
uint32 num_versions = 1;
}
//@@ .. cpp:var:: message All
//@@
//@@ Serve all versions of the model.
//@@
message All {}
//@@ .. cpp:var:: message Specific
//@@
//@@ Serve only specific versions of the model.
//@@
message Specific
{
//@@ .. cpp:var:: int64 versions (repeated)
//@@
//@@ The specific versions of the model that will be served.
//@@
repeated int64 versions = 1;
}
//@@ .. cpp:var:: oneof policy_choice
//@@
//@@ Each model must implement only a single version policy. The
//@@ default policy is 'Latest'.
//@@
oneof policy_choice
{
//@@ .. cpp:var:: Latest latest
//@@
//@@ Serve only latest version(s) of the model.
//@@
Latest latest = 1;
//@@ .. cpp:var:: All all
//@@
//@@ Serve all versions of the model.
//@@
All all = 2;
//@@ .. cpp:var:: Specific specific
//@@
//@@ Serve only specific version(s) of the model.
//@@
Specific specific = 3;
}
}
//@@
//@@.. cpp:var:: message ModelOptimizationPolicy
//@@
//@@ Optimization settings for a model. These settings control if/how a
//@@ model is optimized and prioritized by the backend framework when
//@@ it is loaded.
//@@
message ModelOptimizationPolicy
{
//@@
//@@ .. cpp:var:: message Graph
//@@
//@@ Enable generic graph optimization of the model. If not specified
//@@ the framework's default level of optimization is used. Supports
//@@ TensorFlow graphdef and savedmodel and Onnx models. For TensorFlow
//@@ causes XLA to be enabled/disabled for the model. For Onnx defaults
//@@ to enabling all optimizations, -1 enables only basic optimizations,
//@@ +1 enables only basic and extended optimizations.
//@@
message Graph
{
//@@ .. cpp:var:: int32 level
//@@
//@@ The optimization level. Defaults to 0 (zero) if not specified.
//@@
//@@ - -1: Disabled
//@@ - 0: Framework default
//@@ - 1+: Enable optimization level (greater values indicate
//@@ higher optimization levels)
//@@
int32 level = 1;
}
//@@
//@@ .. cpp:enum:: ModelPriority
//@@
//@@ Model priorities. A model will be given scheduling and execution
//@@ preference over models at lower priorities. Current model
//@@ priorities only work for TensorRT models.
//@@
enum ModelPriority {
//@@ .. cpp:enumerator:: ModelPriority::PRIORITY_DEFAULT = 0
//@@
//@@ The default model priority.
//@@
PRIORITY_DEFAULT = 0;
//@@ .. cpp:enumerator:: ModelPriority::PRIORITY_MAX = 1
//@@
//@@ The maximum model priority.
//@@
PRIORITY_MAX = 1;
//@@ .. cpp:enumerator:: ModelPriority::PRIORITY_MIN = 2
//@@
//@@ The minimum model priority.
//@@
PRIORITY_MIN = 2;
}
//@@
//@@ .. cpp:var:: message Cuda
//@@
//@@ CUDA-specific optimization settings.
//@@
message Cuda
{
//@@ .. cpp:var:: message GraphSpec
//@@
//@@ Specification of the CUDA graph to be captured.
//@@
message GraphSpec
{
//@@ .. cpp:var:: message Dims
//@@
//@@ Specification of tensor dimension.
//@@
message Shape
{
//@@ .. cpp:var:: int64 dim (repeated)
//@@
//@@ The dimension.
//@@
repeated int64 dim = 1;
}
message LowerBound
{
//@@ .. cpp:var:: int32 batch_size
//@@
//@@ The batch size of the CUDA graph. If 'max_batch_size' is 0,
//@@ 'batch_size' must be set to 0. Otherwise, 'batch_size' must
//@@ be set to value between 1 and 'max_batch_size'.
//@@
int32 batch_size = 1;
//@@ .. cpp:var:: map<string, Shape> input
//@@
//@@ The specification of the inputs. 'Shape' is the shape of
//@@ the input without batching dimension.
//@@
map<string, Shape> input = 2;
}
//@@ .. cpp:var:: int32 batch_size
//@@
//@@ The batch size of the CUDA graph. If 'max_batch_size' is 0,
//@@ 'batch_size' must be set to 0. Otherwise, 'batch_size' must
//@@ be set to value between 1 and 'max_batch_size'.
//@@
int32 batch_size = 1;
//@@ .. cpp:var:: map<string, Shape> input
//@@
//@@ The specification of the inputs. 'Shape' is the shape of the
//@@ input without batching dimension.
//@@
map<string, Shape> input = 2;
//@@ .. cpp:var:: LowerBound graph_lower_bound
//@@
//@@ Specify the lower bound of the CUDA graph. Optional.
//@@ If specified, the graph can be used for input shapes and
//@@ batch sizes that are in closed interval between the lower
//@@ bound specification and graph specification. For dynamic
//@@ shape model, this allows CUDA graphs to be launched
//@@ frequently without capturing all possible shape combinations.
//@@ However, using graph for shape combinations different from
//@@ the one used for capturing introduces uninitialized data for
//@@ execution and it may distort the inference result if
//@@ the model is sensitive to uninitialized data.
//@@
LowerBound graph_lower_bound = 3;
}
//@@ .. cpp:var:: bool graphs
//@@
//@@ Use CUDA graphs API to capture model operations and execute
//@@ them more efficiently. Default value is false.
//@@ Currently only recognized by TensorRT backend.
//@@
bool graphs = 1;
//@@ .. cpp:var:: bool busy_wait_events
//@@
//@@ Use busy-waiting to synchronize CUDA events to achieve minimum
//@@ latency from event complete to host thread to be notified, with
//@@ the cost of high CPU load. Default value is false.
//@@ Currently only recognized by TensorRT backend.
//@@
bool busy_wait_events = 2;
//@@ .. cpp:var:: GraphSpec graph_spec (repeated)
//@@
//@@ Specification of the CUDA graph to be captured. If not specified
//@@ and 'graphs' is true, the default CUDA graphs will be captured
//@@ based on model settings.
//@@ Currently only recognized by TensorRT backend.
//@@
repeated GraphSpec graph_spec = 3;
}
//@@
//@@ .. 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
//@@
message ExecutionAccelerators
{
//@@
//@@ .. cpp:var:: message Accelerator
//@@
//@@ Specify the accelerator to be used to execute the model.
//@@ Accelerator with the same name may accept different parameters
//@@ depending on the backends.
//@@
message Accelerator
{
//@@ .. cpp:var:: string name
//@@
//@@ The name of the execution accelerator.
//@@
string name = 1;
//@@ .. cpp:var:: map<string, string> parameters
//@@
//@@ Additional paremeters used to configure the accelerator.
//@@
map<string, string> parameters = 2;
}
//@@ .. 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.
//@@
repeated Accelerator gpu_execution_accelerator = 1;
//@@ .. 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.
//@@
repeated Accelerator cpu_execution_accelerator = 2;
}
//@@
//@@ .. cpp:var:: message PinnedMemoryBuffer
//@@
//@@ Specify whether to use a pinned memory buffer when transferring data
//@@ between non-pinned system memory and GPU memory. Using a pinned
//@@ memory buffer for system from/to GPU transfers will typically provide
//@@ increased performance. For example, in the common use case where the
//@@ request provides inputs and delivers outputs via non-pinned system
//@@ memory, if the model instance accepts GPU IOs, the inputs will be
//@@ processed by two copies: from non-pinned system memory to pinned
//@@ memory, and from pinned memory to GPU memory. Similarly, pinned
//@@ memory will be used for delivering the outputs.
//@@
message PinnedMemoryBuffer
{
//@@ .. cpp:var:: bool enable
//@@
//@@ Use pinned memory buffer. Default is true.
//@@
bool enable = 1;
}
//@@ .. cpp:var:: Graph graph
//@@
//@@ The graph optimization setting for the model. Optional.
//@@
Graph graph = 1;
//@@ .. cpp:var:: ModelPriority priority
//@@
//@@ The priority setting for the model. Optional.
//@@
ModelPriority priority = 2;
//@@ .. cpp:var:: Cuda cuda
//@@
//@@ CUDA-specific optimization settings. Optional.
//@@
Cuda cuda = 3;
//@@ .. cpp:var:: ExecutionAccelerators execution_accelerators
//@@
//@@ The accelerators used for the model. Optional.
//@@
ExecutionAccelerators execution_accelerators = 4;
//@@ .. cpp:var:: PinnedMemoryBuffer input_pinned_memory
//@@
//@@ Use pinned memory buffer when the data transfer for inputs
//@@ is between GPU memory and non-pinned system memory.
//@@ Default is true.
//@@
PinnedMemoryBuffer input_pinned_memory = 5;
//@@ .. cpp:var:: PinnedMemoryBuffer output_pinned_memory
//@@
//@@ Use pinned memory buffer when the data transfer for outputs
//@@ is between GPU memory and non-pinned system memory.
//@@ Default is true.
//@@
PinnedMemoryBuffer output_pinned_memory = 6;
}
//@@
//@@.. cpp:var:: message ModelQueuePolicy
//@@
//@@ Queue policy for inference requests.
//@@
message ModelQueuePolicy
{
//@@
//@@ .. cpp:enum:: TimeoutAction
//@@
//@@ The action applied to timed-out requests.
//@@
enum TimeoutAction {
//@@ .. cpp:enumerator:: Action::REJECT = 0
//@@
//@@ Reject the request and return error message accordingly.
//@@
REJECT = 0;
//@@ .. cpp:enumerator:: Action::DELAY = 1
//@@
//@@ Delay the request until all other requests at the same
//@@ (or higher) priority levels that have not reached their timeouts
//@@ are processed. A delayed request will eventually be processed,
//@@ but may be delayed indefinitely due to newly arriving requests.
//@@
DELAY = 1;
}
//@@
//@@ .. cpp:var:: TimeoutAction timeout_action
//@@
//@@ The action applied to timed-out request.
//@@ The default action is REJECT.
//@@
TimeoutAction timeout_action = 1;
//@@
//@@ .. cpp:var:: uint64 default_timeout_microseconds
//@@
//@@ The default timeout for every request, in microseconds.
//@@ The default value is 0 which indicates that no timeout is set.
//@@
uint64 default_timeout_microseconds = 2;
//@@
//@@ .. cpp:var:: bool allow_timeout_override
//@@
//@@ Whether individual request can override the default timeout value.
//@@ When true, individual requests can set a timeout that is less than
//@@ the default timeout value but may not increase the timeout.
//@@ The default value is false.
//@@
bool allow_timeout_override = 3;
//@@
//@@ .. cpp:var:: uint32 max_queue_size
//@@
//@@ The maximum queue size for holding requests. A request will be
//@@ rejected immediately if it can't be enqueued because the queue is
//@@ full. The default value is 0 which indicates that no maximum
//@@ queue size is enforced.
//@@
uint32 max_queue_size = 4;
}
//@@
//@@.. cpp:var:: message ModelDynamicBatching
//@@
//@@ Dynamic batching configuration. These settings control how dynamic
//@@ batching operates for the model.
//@@
message ModelDynamicBatching
{
//@@ .. cpp:var:: int32 preferred_batch_size (repeated)
//@@
//@@ Preferred batch sizes for dynamic batching. If a batch of one of
//@@ these sizes can be formed it will be executed immediately. If
//@@ not specified a preferred batch size will be chosen automatically
//@@ based on model and GPU characteristics.
//@@
repeated int32 preferred_batch_size = 1;
//@@ .. cpp:var:: uint64 max_queue_delay_microseconds
//@@
//@@ The maximum time, in microseconds, a request will be delayed in
//@@ the scheduling queue to wait for additional requests for
//@@ batching. Default is 0.
//@@
uint64 max_queue_delay_microseconds = 2;
//@@ .. cpp:var:: bool preserve_ordering
//@@