/
model_config.pb.go
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/
model_config.pb.go
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// Copyright 2018-2023, NVIDIA CORPORATION & AFFILIATES. 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.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.28.1
// protoc v3.21.6
// source: pkg/apis/inference/model_config.proto
package inference
import (
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
reflect "reflect"
sync "sync"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// @@
// @@.. cpp:enum:: DataType
// @@
// @@ Data types supported for input and output tensors.
// @@
type DataType int32
const (
// @@ .. cpp:enumerator:: DataType::INVALID = 0
DataType_TYPE_INVALID DataType = 0
// @@ .. cpp:enumerator:: DataType::BOOL = 1
DataType_TYPE_BOOL DataType = 1
// @@ .. cpp:enumerator:: DataType::UINT8 = 2
DataType_TYPE_UINT8 DataType = 2
// @@ .. cpp:enumerator:: DataType::UINT16 = 3
DataType_TYPE_UINT16 DataType = 3
// @@ .. cpp:enumerator:: DataType::UINT32 = 4
DataType_TYPE_UINT32 DataType = 4
// @@ .. cpp:enumerator:: DataType::UINT64 = 5
DataType_TYPE_UINT64 DataType = 5
// @@ .. cpp:enumerator:: DataType::INT8 = 6
DataType_TYPE_INT8 DataType = 6
// @@ .. cpp:enumerator:: DataType::INT16 = 7
DataType_TYPE_INT16 DataType = 7
// @@ .. cpp:enumerator:: DataType::INT32 = 8
DataType_TYPE_INT32 DataType = 8
// @@ .. cpp:enumerator:: DataType::INT64 = 9
DataType_TYPE_INT64 DataType = 9
// @@ .. cpp:enumerator:: DataType::FP16 = 10
DataType_TYPE_FP16 DataType = 10
// @@ .. cpp:enumerator:: DataType::FP32 = 11
DataType_TYPE_FP32 DataType = 11
// @@ .. cpp:enumerator:: DataType::FP64 = 12
DataType_TYPE_FP64 DataType = 12
// @@ .. cpp:enumerator:: DataType::STRING = 13
DataType_TYPE_STRING DataType = 13
// @@ .. cpp:enumerator:: DataType::BF16 = 14
DataType_TYPE_BF16 DataType = 14
)
// Enum value maps for DataType.
var (
DataType_name = map[int32]string{
0: "TYPE_INVALID",
1: "TYPE_BOOL",
2: "TYPE_UINT8",
3: "TYPE_UINT16",
4: "TYPE_UINT32",
5: "TYPE_UINT64",
6: "TYPE_INT8",
7: "TYPE_INT16",
8: "TYPE_INT32",
9: "TYPE_INT64",
10: "TYPE_FP16",
11: "TYPE_FP32",
12: "TYPE_FP64",
13: "TYPE_STRING",
14: "TYPE_BF16",
}
DataType_value = map[string]int32{
"TYPE_INVALID": 0,
"TYPE_BOOL": 1,
"TYPE_UINT8": 2,
"TYPE_UINT16": 3,
"TYPE_UINT32": 4,
"TYPE_UINT64": 5,
"TYPE_INT8": 6,
"TYPE_INT16": 7,
"TYPE_INT32": 8,
"TYPE_INT64": 9,
"TYPE_FP16": 10,
"TYPE_FP32": 11,
"TYPE_FP64": 12,
"TYPE_STRING": 13,
"TYPE_BF16": 14,
}
)
func (x DataType) Enum() *DataType {
p := new(DataType)
*p = x
return p
}
func (x DataType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (DataType) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[0].Descriptor()
}
func (DataType) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[0]
}
func (x DataType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use DataType.Descriptor instead.
func (DataType) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{0}
}
// @@
// @@ .. cpp:enum:: Kind
// @@
// @@ Kind of this instance group.
// @@
type ModelInstanceGroup_Kind int32
const (
// @@ .. 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.
// @@
ModelInstanceGroup_KIND_AUTO ModelInstanceGroup_Kind = 0
// @@ .. cpp:enumerator:: Kind::KIND_GPU = 1
// @@
// @@ This instance group represents instances that must run on the
// @@ GPU.
// @@
ModelInstanceGroup_KIND_GPU ModelInstanceGroup_Kind = 1
// @@ .. cpp:enumerator:: Kind::KIND_CPU = 2
// @@
// @@ This instance group represents instances that must run on the
// @@ CPU.
// @@
ModelInstanceGroup_KIND_CPU ModelInstanceGroup_Kind = 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.
// @@
ModelInstanceGroup_KIND_MODEL ModelInstanceGroup_Kind = 3
)
// Enum value maps for ModelInstanceGroup_Kind.
var (
ModelInstanceGroup_Kind_name = map[int32]string{
0: "KIND_AUTO",
1: "KIND_GPU",
2: "KIND_CPU",
3: "KIND_MODEL",
}
ModelInstanceGroup_Kind_value = map[string]int32{
"KIND_AUTO": 0,
"KIND_GPU": 1,
"KIND_CPU": 2,
"KIND_MODEL": 3,
}
)
func (x ModelInstanceGroup_Kind) Enum() *ModelInstanceGroup_Kind {
p := new(ModelInstanceGroup_Kind)
*p = x
return p
}
func (x ModelInstanceGroup_Kind) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelInstanceGroup_Kind) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[1].Descriptor()
}
func (ModelInstanceGroup_Kind) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[1]
}
func (x ModelInstanceGroup_Kind) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelInstanceGroup_Kind.Descriptor instead.
func (ModelInstanceGroup_Kind) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{1, 0}
}
// @@
// @@ .. cpp:enum:: SecondaryDeviceKind
// @@
// @@ The kind of the secondary device.
// @@
type ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind int32
const (
// @@ .. cpp:enumerator:: SecondaryDeviceKind::KIND_NVDLA = 0
// @@
// @@ An NVDLA core. http://nvdla.org
// @@ Currently KIND_NVDLA is only supported by the TensorRT backend.
// @@
ModelInstanceGroup_SecondaryDevice_KIND_NVDLA ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind = 0
)
// Enum value maps for ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind.
var (
ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind_name = map[int32]string{
0: "KIND_NVDLA",
}
ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind_value = map[string]int32{
"KIND_NVDLA": 0,
}
)
func (x ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) Enum() *ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind {
p := new(ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind)
*p = x
return p
}
func (x ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[2].Descriptor()
}
func (ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[2]
}
func (x ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind.Descriptor instead.
func (ModelInstanceGroup_SecondaryDevice_SecondaryDeviceKind) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{1, 0, 0}
}
// @@
// @@ .. cpp:enum:: Format
// @@
// @@ The format for the input.
// @@
type ModelInput_Format int32
const (
// @@ .. cpp:enumerator:: Format::FORMAT_NONE = 0
// @@
// @@ The input has no specific format. This is the default.
// @@
ModelInput_FORMAT_NONE ModelInput_Format = 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).
// @@
ModelInput_FORMAT_NHWC ModelInput_Format = 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).
// @@
ModelInput_FORMAT_NCHW ModelInput_Format = 2
)
// Enum value maps for ModelInput_Format.
var (
ModelInput_Format_name = map[int32]string{
0: "FORMAT_NONE",
1: "FORMAT_NHWC",
2: "FORMAT_NCHW",
}
ModelInput_Format_value = map[string]int32{
"FORMAT_NONE": 0,
"FORMAT_NHWC": 1,
"FORMAT_NCHW": 2,
}
)
func (x ModelInput_Format) Enum() *ModelInput_Format {
p := new(ModelInput_Format)
*p = x
return p
}
func (x ModelInput_Format) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelInput_Format) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[3].Descriptor()
}
func (ModelInput_Format) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[3]
}
func (x ModelInput_Format) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelInput_Format.Descriptor instead.
func (ModelInput_Format) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{3, 0}
}
// @@
// @@ .. cpp:enum:: Kind
// @@
// @@ The kind of the batch input.
// @@
type BatchInput_Kind int32
const (
// @@ .. cpp:enumerator:: Kind::BATCH_ELEMENT_COUNT = 0
// @@
// @@ The element count of the 'source_input' will be added as
// @@ input with shape [1].
// @@
BatchInput_BATCH_ELEMENT_COUNT BatchInput_Kind = 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.
// @@
BatchInput_BATCH_ACCUMULATED_ELEMENT_COUNT BatchInput_Kind = 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.
// @@
BatchInput_BATCH_ACCUMULATED_ELEMENT_COUNT_WITH_ZERO BatchInput_Kind = 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.
// @@
BatchInput_BATCH_MAX_ELEMENT_COUNT_AS_SHAPE BatchInput_Kind = 3
// @@ .. cpp:enumerator:: Kind::BATCH_ITEM_SHAPE = 4
// @@
// @@ Among the requests in the batch, the shape of the
// @@ 'source_input' will be added as input with shape
// @@ [batch_size, len(input_dim)]. For example, if one
// @@ batch-2 input with shape [3, 1] and batch-1 input
// @@ with shape [2, 2] are batched, the batch input will
// @@ have shape [3, 2] and value [ [3, 1], [3, 1], [2, 2]].
// @@
BatchInput_BATCH_ITEM_SHAPE BatchInput_Kind = 4
// @@ .. cpp:enumerator:: Kind::BATCH_ITEM_SHAPE_FLATTEN = 5
// @@
// @@ Among the requests in the batch, the shape of the
// @@ 'source_input' will be added as input with single dimensional
// @@ shape [batch_size * len(input_dim)]. For example, if one
// @@ batch-2 input with shape [3, 1] and batch-1 input
// @@ with shape [2, 2] are batched, the batch input will
// @@ have shape [6] and value [3, 1, 3, 1, 2, 2].
// @@
BatchInput_BATCH_ITEM_SHAPE_FLATTEN BatchInput_Kind = 5
)
// Enum value maps for BatchInput_Kind.
var (
BatchInput_Kind_name = map[int32]string{
0: "BATCH_ELEMENT_COUNT",
1: "BATCH_ACCUMULATED_ELEMENT_COUNT",
2: "BATCH_ACCUMULATED_ELEMENT_COUNT_WITH_ZERO",
3: "BATCH_MAX_ELEMENT_COUNT_AS_SHAPE",
4: "BATCH_ITEM_SHAPE",
5: "BATCH_ITEM_SHAPE_FLATTEN",
}
BatchInput_Kind_value = map[string]int32{
"BATCH_ELEMENT_COUNT": 0,
"BATCH_ACCUMULATED_ELEMENT_COUNT": 1,
"BATCH_ACCUMULATED_ELEMENT_COUNT_WITH_ZERO": 2,
"BATCH_MAX_ELEMENT_COUNT_AS_SHAPE": 3,
"BATCH_ITEM_SHAPE": 4,
"BATCH_ITEM_SHAPE_FLATTEN": 5,
}
)
func (x BatchInput_Kind) Enum() *BatchInput_Kind {
p := new(BatchInput_Kind)
*p = x
return p
}
func (x BatchInput_Kind) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (BatchInput_Kind) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[4].Descriptor()
}
func (BatchInput_Kind) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[4]
}
func (x BatchInput_Kind) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use BatchInput_Kind.Descriptor instead.
func (BatchInput_Kind) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{5, 0}
}
// @@
// @@ .. cpp:enum:: Kind
// @@
// @@ The kind of the batch output.
// @@
type BatchOutput_Kind int32
const (
// @@ .. 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.
// @@
BatchOutput_BATCH_SCATTER_WITH_INPUT_SHAPE BatchOutput_Kind = 0
)
// Enum value maps for BatchOutput_Kind.
var (
BatchOutput_Kind_name = map[int32]string{
0: "BATCH_SCATTER_WITH_INPUT_SHAPE",
}
BatchOutput_Kind_value = map[string]int32{
"BATCH_SCATTER_WITH_INPUT_SHAPE": 0,
}
)
func (x BatchOutput_Kind) Enum() *BatchOutput_Kind {
p := new(BatchOutput_Kind)
*p = x
return p
}
func (x BatchOutput_Kind) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (BatchOutput_Kind) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[5].Descriptor()
}
func (BatchOutput_Kind) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[5]
}
func (x BatchOutput_Kind) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use BatchOutput_Kind.Descriptor instead.
func (BatchOutput_Kind) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{6, 0}
}
// @@
// @@ .. 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.
// @@
type ModelOptimizationPolicy_ModelPriority int32
const (
// @@ .. cpp:enumerator:: ModelPriority::PRIORITY_DEFAULT = 0
// @@
// @@ The default model priority.
// @@
ModelOptimizationPolicy_PRIORITY_DEFAULT ModelOptimizationPolicy_ModelPriority = 0
// @@ .. cpp:enumerator:: ModelPriority::PRIORITY_MAX = 1
// @@
// @@ The maximum model priority.
// @@
ModelOptimizationPolicy_PRIORITY_MAX ModelOptimizationPolicy_ModelPriority = 1
// @@ .. cpp:enumerator:: ModelPriority::PRIORITY_MIN = 2
// @@
// @@ The minimum model priority.
// @@
ModelOptimizationPolicy_PRIORITY_MIN ModelOptimizationPolicy_ModelPriority = 2
)
// Enum value maps for ModelOptimizationPolicy_ModelPriority.
var (
ModelOptimizationPolicy_ModelPriority_name = map[int32]string{
0: "PRIORITY_DEFAULT",
1: "PRIORITY_MAX",
2: "PRIORITY_MIN",
}
ModelOptimizationPolicy_ModelPriority_value = map[string]int32{
"PRIORITY_DEFAULT": 0,
"PRIORITY_MAX": 1,
"PRIORITY_MIN": 2,
}
)
func (x ModelOptimizationPolicy_ModelPriority) Enum() *ModelOptimizationPolicy_ModelPriority {
p := new(ModelOptimizationPolicy_ModelPriority)
*p = x
return p
}
func (x ModelOptimizationPolicy_ModelPriority) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelOptimizationPolicy_ModelPriority) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[6].Descriptor()
}
func (ModelOptimizationPolicy_ModelPriority) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[6]
}
func (x ModelOptimizationPolicy_ModelPriority) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelOptimizationPolicy_ModelPriority.Descriptor instead.
func (ModelOptimizationPolicy_ModelPriority) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{8, 0}
}
// @@
// @@ .. cpp:enum:: TimeoutAction
// @@
// @@ The action applied to timed-out requests.
// @@
type ModelQueuePolicy_TimeoutAction int32
const (
// @@ .. cpp:enumerator:: Action::REJECT = 0
// @@
// @@ Reject the request and return error message accordingly.
// @@
ModelQueuePolicy_REJECT ModelQueuePolicy_TimeoutAction = 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.
// @@
ModelQueuePolicy_DELAY ModelQueuePolicy_TimeoutAction = 1
)
// Enum value maps for ModelQueuePolicy_TimeoutAction.
var (
ModelQueuePolicy_TimeoutAction_name = map[int32]string{
0: "REJECT",
1: "DELAY",
}
ModelQueuePolicy_TimeoutAction_value = map[string]int32{
"REJECT": 0,
"DELAY": 1,
}
)
func (x ModelQueuePolicy_TimeoutAction) Enum() *ModelQueuePolicy_TimeoutAction {
p := new(ModelQueuePolicy_TimeoutAction)
*p = x
return p
}
func (x ModelQueuePolicy_TimeoutAction) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelQueuePolicy_TimeoutAction) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[7].Descriptor()
}
func (ModelQueuePolicy_TimeoutAction) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[7]
}
func (x ModelQueuePolicy_TimeoutAction) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelQueuePolicy_TimeoutAction.Descriptor instead.
func (ModelQueuePolicy_TimeoutAction) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{9, 0}
}
// @@
// @@ .. cpp:enum:: Kind
// @@
// @@ The kind of the control.
// @@
type ModelSequenceBatching_Control_Kind int32
const (
// @@ .. cpp:enumerator:: Kind::CONTROL_SEQUENCE_START = 0
// @@
// @@ A new sequence is/is-not starting. If true a sequence is
// @@ starting, if false a sequence is continuing. Must
// @@ specify either int32_false_true, fp32_false_true or
// @@ bool_false_true for this control. This control is optional.
// @@
ModelSequenceBatching_Control_CONTROL_SEQUENCE_START ModelSequenceBatching_Control_Kind = 0
// @@ .. cpp:enumerator:: Kind::CONTROL_SEQUENCE_READY = 1
// @@
// @@ A sequence is/is-not ready for inference. If true the
// @@ input tensor data is valid and should be used. If false
// @@ the input tensor data is invalid and inferencing should
// @@ be "skipped". Must specify either int32_false_true,
// @@ fp32_false_true or bool_false_true for this control. This
// @@ control is optional.
// @@
ModelSequenceBatching_Control_CONTROL_SEQUENCE_READY ModelSequenceBatching_Control_Kind = 1
// @@ .. cpp:enumerator:: Kind::CONTROL_SEQUENCE_END = 2
// @@
// @@ A sequence is/is-not ending. If true a sequence is
// @@ ending, if false a sequence is continuing. Must specify
// @@ either int32_false_true, fp32_false_true or bool_false_true
// @@ for this control. This control is optional.
// @@
ModelSequenceBatching_Control_CONTROL_SEQUENCE_END ModelSequenceBatching_Control_Kind = 2
// @@ .. cpp:enumerator:: Kind::CONTROL_SEQUENCE_CORRID = 3
// @@
// @@ The correlation ID of the sequence. The correlation ID
// @@ is an uint64_t value that is communicated in whole or
// @@ in part by the tensor. The tensor's datatype must be
// @@ specified by data_type and must be TYPE_UINT64, TYPE_INT64,
// @@ TYPE_UINT32 or TYPE_INT32. If a 32-bit datatype is specified
// @@ the correlation ID will be truncated to the low-order 32
// @@ bits. This control is optional.
// @@
ModelSequenceBatching_Control_CONTROL_SEQUENCE_CORRID ModelSequenceBatching_Control_Kind = 3
)
// Enum value maps for ModelSequenceBatching_Control_Kind.
var (
ModelSequenceBatching_Control_Kind_name = map[int32]string{
0: "CONTROL_SEQUENCE_START",
1: "CONTROL_SEQUENCE_READY",
2: "CONTROL_SEQUENCE_END",
3: "CONTROL_SEQUENCE_CORRID",
}
ModelSequenceBatching_Control_Kind_value = map[string]int32{
"CONTROL_SEQUENCE_START": 0,
"CONTROL_SEQUENCE_READY": 1,
"CONTROL_SEQUENCE_END": 2,
"CONTROL_SEQUENCE_CORRID": 3,
}
)
func (x ModelSequenceBatching_Control_Kind) Enum() *ModelSequenceBatching_Control_Kind {
p := new(ModelSequenceBatching_Control_Kind)
*p = x
return p
}
func (x ModelSequenceBatching_Control_Kind) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (ModelSequenceBatching_Control_Kind) Descriptor() protoreflect.EnumDescriptor {
return file_pkg_apis_inference_model_config_proto_enumTypes[8].Descriptor()
}
func (ModelSequenceBatching_Control_Kind) Type() protoreflect.EnumType {
return &file_pkg_apis_inference_model_config_proto_enumTypes[8]
}
func (x ModelSequenceBatching_Control_Kind) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use ModelSequenceBatching_Control_Kind.Descriptor instead.
func (ModelSequenceBatching_Control_Kind) EnumDescriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{11, 0, 0}
}
// @@
// @@ .. cpp:var:: message ModelRateLimiter
// @@
// @@ The specifications required by the rate limiter to properly
// @@ schedule the inference requests across the different models
// @@ and their instances.
// @@
type ModelRateLimiter struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// @@ .. 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.
// @@
Resources []*ModelRateLimiter_Resource `protobuf:"bytes,1,rep,name=resources,proto3" json:"resources,omitempty"`
// @@ .. cpp:var:: uint32 priority
// @@
// @@ The optional 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. The priority of value 0 will be
// @@ treated as priority 1.
// @@
Priority uint32 `protobuf:"varint,2,opt,name=priority,proto3" json:"priority,omitempty"`
}
func (x *ModelRateLimiter) Reset() {
*x = ModelRateLimiter{}
if protoimpl.UnsafeEnabled {
mi := &file_pkg_apis_inference_model_config_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelRateLimiter) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelRateLimiter) ProtoMessage() {}
func (x *ModelRateLimiter) ProtoReflect() protoreflect.Message {
mi := &file_pkg_apis_inference_model_config_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelRateLimiter.ProtoReflect.Descriptor instead.
func (*ModelRateLimiter) Descriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{0}
}
func (x *ModelRateLimiter) GetResources() []*ModelRateLimiter_Resource {
if x != nil {
return x.Resources
}
return nil
}
func (x *ModelRateLimiter) GetPriority() uint32 {
if x != nil {
return x.Priority
}
return 0
}
// @@
// @@.. cpp:var:: message ModelInstanceGroup
// @@
// @@ A group of one or more instances of a model and resources made
// @@ available for those instances.
// @@
type ModelInstanceGroup struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// @@ .. 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:
// @@
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// @@ .. 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 ModelInstanceGroup_Kind `protobuf:"varint,4,opt,name=kind,proto3,enum=inference.ModelInstanceGroup_Kind" json:"kind,omitempty"`
// @@ .. 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.
Count int32 `protobuf:"varint,2,opt,name=count,proto3" json:"count,omitempty"`
// @@ .. 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.
// @@
RateLimiter *ModelRateLimiter `protobuf:"bytes,6,opt,name=rate_limiter,json=rateLimiter,proto3" json:"rate_limiter,omitempty"`
// @@ .. 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.
// @@
Gpus []int32 `protobuf:"varint,3,rep,packed,name=gpus,proto3" json:"gpus,omitempty"`
// @@ .. cpp:var:: SecondaryDevice secondary_devices (repeated)
// @@
// @@ Secondary devices that are required by instances specified by this
// @@ instance group. Optional.
// @@
SecondaryDevices []*ModelInstanceGroup_SecondaryDevice `protobuf:"bytes,8,rep,name=secondary_devices,json=secondaryDevices,proto3" json:"secondary_devices,omitempty"`
// @@ .. 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.
// @@
Profile []string `protobuf:"bytes,5,rep,name=profile,proto3" json:"profile,omitempty"`
// @@ .. cpp:var:: bool passive
// @@
// @@ Whether the instances within this instance group will be accepting
// @@ inference requests from the scheduler. If true, the instances will
// @@ not be added to the scheduler. Default value is false.
// @@
Passive bool `protobuf:"varint,7,opt,name=passive,proto3" json:"passive,omitempty"`
// @@ .. cpp:var:: string host_policy
// @@
// @@ The host policy name that the instance to be associated with.
// @@ The default value is set to reflect the device kind of the instance,
// @@ for instance, KIND_CPU is "cpu", KIND_MODEL is "model" and
// @@ KIND_GPU is "gpu_<gpu_id>".
// @@
HostPolicy string `protobuf:"bytes,9,opt,name=host_policy,json=hostPolicy,proto3" json:"host_policy,omitempty"`
}
func (x *ModelInstanceGroup) Reset() {
*x = ModelInstanceGroup{}
if protoimpl.UnsafeEnabled {
mi := &file_pkg_apis_inference_model_config_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *ModelInstanceGroup) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*ModelInstanceGroup) ProtoMessage() {}
func (x *ModelInstanceGroup) ProtoReflect() protoreflect.Message {
mi := &file_pkg_apis_inference_model_config_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use ModelInstanceGroup.ProtoReflect.Descriptor instead.
func (*ModelInstanceGroup) Descriptor() ([]byte, []int) {
return file_pkg_apis_inference_model_config_proto_rawDescGZIP(), []int{1}
}
func (x *ModelInstanceGroup) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *ModelInstanceGroup) GetKind() ModelInstanceGroup_Kind {
if x != nil {
return x.Kind
}
return ModelInstanceGroup_KIND_AUTO
}
func (x *ModelInstanceGroup) GetCount() int32 {
if x != nil {
return x.Count
}
return 0
}
func (x *ModelInstanceGroup) GetRateLimiter() *ModelRateLimiter {
if x != nil {
return x.RateLimiter
}
return nil
}
func (x *ModelInstanceGroup) GetGpus() []int32 {
if x != nil {
return x.Gpus
}
return nil
}
func (x *ModelInstanceGroup) GetSecondaryDevices() []*ModelInstanceGroup_SecondaryDevice {
if x != nil {
return x.SecondaryDevices
}
return nil
}
func (x *ModelInstanceGroup) GetProfile() []string {
if x != nil {
return x.Profile
}
return nil
}
func (x *ModelInstanceGroup) GetPassive() bool {
if x != nil {
return x.Passive
}
return false
}
func (x *ModelInstanceGroup) GetHostPolicy() string {
if x != nil {
return x.HostPolicy
}
return ""