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feat: add gkerecommender GkeInferenceQuickstart API v1

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Google APIs 2025-08-22 10:12:28 -07:00 • committed by Copybara-Service
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google/cloud/gkerecommender/v1/BUILD.bazel Normal file
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# This file was automatically generated by BuildFileGenerator
# https://github.com/googleapis/rules_gapic/tree/master/bazel
# Most of the manual changes to this file will be overwritten.
# It's **only** allowed to change the following rule attribute values:
# - names of *_gapic_assembly_* rules
# - certain parameters of *_gapic_library rules, including but not limited to:
# * extra_protoc_parameters
# * extra_protoc_file_parameters
# The complete list of preserved parameters can be found in the source code.
# buildifier: disable=load-on-top
# This is an API workspace, having public visibility by default makes perfect sense.
package(default_visibility = ["//visibility:public"])
##############################################################################
# Common
##############################################################################
# buildifier: disable=same-origin-load
load("@com_google_googleapis_imports//:imports.bzl", "proto_library_with_info")
load("@rules_proto//proto:defs.bzl", "proto_library")
proto_library(
name = "gkerecommender_proto",
srcs = [
"gkerecommender.proto",
],
deps = [
"//google/api:annotations_proto",
"//google/api:client_proto",
"//google/api:field_behavior_proto",
],
)
proto_library_with_info(
name = "gkerecommender_proto_with_info",
deps = [
":gkerecommender_proto",
"//google/cloud:common_resources_proto",
],
)
##############################################################################
# Java
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"java_gapic_assembly_gradle_pkg",
"java_gapic_library",
"java_gapic_test",
"java_grpc_library",
"java_proto_library",
)
java_proto_library(
name = "gkerecommender_java_proto",
deps = [":gkerecommender_proto"],
)
java_grpc_library(
name = "gkerecommender_java_grpc",
srcs = [":gkerecommender_proto"],
deps = [":gkerecommender_java_proto"],
)
java_gapic_library(
name = "gkerecommender_java_gapic",
srcs = [":gkerecommender_proto_with_info"],
gapic_yaml = None,
grpc_service_config = "gkerecommender_grpc_service_config.json",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
test_deps = [
":gkerecommender_java_grpc",
],
transport = "grpc+rest",
deps = [
":gkerecommender_java_proto",
"//google/api:api_java_proto",
],
)
java_gapic_test(
name = "gkerecommender_java_gapic_test_suite",
test_classes = [
"com.google.cloud.gkerecommender.v1.GkeInferenceQuickstartClientHttpJsonTest",
"com.google.cloud.gkerecommender.v1.GkeInferenceQuickstartClientTest",
],
runtime_deps = [":gkerecommender_java_gapic_test"],
)
# Open Source Packages
java_gapic_assembly_gradle_pkg(
name = "google-cloud-gkerecommender-v1-java",
include_samples = True,
transport = "grpc+rest",
deps = [
":gkerecommender_java_gapic",
":gkerecommender_java_grpc",
":gkerecommender_java_proto",
":gkerecommender_proto",
],
)
##############################################################################
# Go
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"go_gapic_assembly_pkg",
"go_gapic_library",
"go_proto_library",
)
go_proto_library(
name = "gkerecommender_go_proto",
compilers = ["@io_bazel_rules_go//proto:go_grpc"],
importpath = "cloud.google.com/go/gkerecommender/apiv1/gkerecommenderpb",
protos = [":gkerecommender_proto"],
deps = [
"//google/api:annotations_go_proto",
],
)
go_gapic_library(
name = "gkerecommender_go_gapic",
srcs = [":gkerecommender_proto_with_info"],
grpc_service_config = "gkerecommender_grpc_service_config.json",
importpath = "cloud.google.com/go/gkerecommender/apiv1;gkerecommender",
metadata = True,
release_level = "beta",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [
":gkerecommender_go_proto",
],
)
# Open Source Packages
go_gapic_assembly_pkg(
name = "gapi-cloud-gkerecommender-v1-go",
deps = [
":gkerecommender_go_gapic",
":gkerecommender_go_gapic_srcjar-metadata.srcjar",
":gkerecommender_go_gapic_srcjar-snippets.srcjar",
":gkerecommender_go_gapic_srcjar-test.srcjar",
":gkerecommender_go_proto",
],
)
##############################################################################
# Python
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"py_gapic_assembly_pkg",
"py_gapic_library",
"py_test",
)
py_gapic_library(
name = "gkerecommender_py_gapic",
srcs = [":gkerecommender_proto"],
grpc_service_config = "gkerecommender_grpc_service_config.json",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [],
)
py_test(
name = "gkerecommender_py_gapic_test",
srcs = [
"gkerecommender_py_gapic_pytest.py",
"gkerecommender_py_gapic_test.py",
],
legacy_create_init = False,
deps = [":gkerecommender_py_gapic"],
)
# Open Source Packages
py_gapic_assembly_pkg(
name = "gkerecommender-v1-py",
deps = [
":gkerecommender_py_gapic",
],
)
##############################################################################
# PHP
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"php_gapic_assembly_pkg",
"php_gapic_library",
"php_proto_library",
)
php_proto_library(
name = "gkerecommender_php_proto",
deps = [":gkerecommender_proto"],
)
php_gapic_library(
name = "gkerecommender_php_gapic",
srcs = [":gkerecommender_proto_with_info"],
grpc_service_config = "gkerecommender_grpc_service_config.json",
migration_mode = "NEW_SURFACE_ONLY",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [
":gkerecommender_php_proto",
],
)
# Open Source Packages
php_gapic_assembly_pkg(
name = "google-cloud-gkerecommender-v1-php",
deps = [
":gkerecommender_php_gapic",
":gkerecommender_php_proto",
],
)
##############################################################################
# Node.js
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"nodejs_gapic_assembly_pkg",
"nodejs_gapic_library",
)
nodejs_gapic_library(
name = "gkerecommender_nodejs_gapic",
package_name = "@google-cloud/gkerecommender",
src = ":gkerecommender_proto_with_info",
extra_protoc_parameters = ["metadata"],
grpc_service_config = "gkerecommender_grpc_service_config.json",
package = "google.cloud.gkerecommender.v1",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [],
)
nodejs_gapic_assembly_pkg(
name = "gkerecommender-v1-nodejs",
deps = [
":gkerecommender_nodejs_gapic",
":gkerecommender_proto",
],
)
##############################################################################
# Ruby
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"ruby_cloud_gapic_library",
"ruby_gapic_assembly_pkg",
"ruby_grpc_library",
"ruby_proto_library",
)
ruby_proto_library(
name = "gkerecommender_ruby_proto",
deps = [":gkerecommender_proto"],
)
ruby_grpc_library(
name = "gkerecommender_ruby_grpc",
srcs = [":gkerecommender_proto"],
deps = [":gkerecommender_ruby_proto"],
)
ruby_cloud_gapic_library(
name = "gkerecommender_ruby_gapic",
srcs = [":gkerecommender_proto_with_info"],
extra_protoc_parameters = [
"ruby-cloud-gem-name=google-cloud-gkerecommender-v1",
],
grpc_service_config = "gkerecommender_grpc_service_config.json",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [
":gkerecommender_ruby_grpc",
":gkerecommender_ruby_proto",
],
)
# Open Source Packages
ruby_gapic_assembly_pkg(
name = "google-cloud-gkerecommender-v1-ruby",
deps = [
":gkerecommender_ruby_gapic",
":gkerecommender_ruby_grpc",
":gkerecommender_ruby_proto",
],
)
##############################################################################
# C#
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"csharp_gapic_assembly_pkg",
"csharp_gapic_library",
"csharp_grpc_library",
"csharp_proto_library",
)
csharp_proto_library(
name = "gkerecommender_csharp_proto",
extra_opts = [],
deps = [":gkerecommender_proto"],
)
csharp_grpc_library(
name = "gkerecommender_csharp_grpc",
srcs = [":gkerecommender_proto"],
deps = [":gkerecommender_csharp_proto"],
)
csharp_gapic_library(
name = "gkerecommender_csharp_gapic",
srcs = [":gkerecommender_proto_with_info"],
common_resources_config = "@gax_dotnet//:Google.Api.Gax/ResourceNames/CommonResourcesConfig.json",
grpc_service_config = "gkerecommender_grpc_service_config.json",
rest_numeric_enums = True,
service_yaml = "gkerecommender_v1.yaml",
transport = "grpc+rest",
deps = [
":gkerecommender_csharp_grpc",
":gkerecommender_csharp_proto",
],
)
# Open Source Packages
csharp_gapic_assembly_pkg(
name = "google-cloud-gkerecommender-v1-csharp",
deps = [
":gkerecommender_csharp_gapic",
":gkerecommender_csharp_grpc",
":gkerecommender_csharp_proto",
],
)
##############################################################################
# C++
##############################################################################
# buildifier: disable=same-origin-load
load(
"@com_google_googleapis_imports//:imports.bzl",
"cc_grpc_library",
"cc_proto_library",
)
cc_proto_library(
name = "gkerecommender_cc_proto",
deps = [":gkerecommender_proto"],
)
cc_grpc_library(
name = "gkerecommender_cc_grpc",
srcs = [":gkerecommender_proto"],
grpc_only = True,
deps = [":gkerecommender_cc_proto"],
)

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google/cloud/gkerecommender/v1/gkerecommender.proto Normal file
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// Copyright 2025 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package google.cloud.gkerecommender.v1;
import "google/api/annotations.proto";
import "google/api/client.proto";
import "google/api/field_behavior.proto";
option csharp_namespace = "Google.Cloud.GkeRecommender.V1";
option go_package = "cloud.google.com/go/gkerecommender/apiv1/gkerecommenderpb;gkerecommenderpb";
option java_multiple_files = true;
option java_outer_classname = "GkeRecommenderProto";
option java_package = "com.google.cloud.gkerecommender.v1";
option php_namespace = "Google\\Cloud\\GkeRecommender\\V1";
option ruby_package = "Google::Cloud::GkeRecommender::V1";
// GKE Inference Quickstart (GIQ) service provides profiles with performance
// metrics for popular models and model servers across multiple accelerators.
// These profiles help generate optimized best practices for running inference
// on GKE.
service GkeInferenceQuickstart {
option (google.api.default_host) = "gkerecommender.googleapis.com";
option (google.api.oauth_scopes) =
"https://www.googleapis.com/auth/cloud-platform";
// Fetches available models. Open-source models follow the Huggingface Hub
// `owner/model_name` format.
rpc FetchModels(FetchModelsRequest) returns (FetchModelsResponse) {
option (google.api.http) = {
get: "/v1/models:fetch"
};
}
// Fetches available model servers. Open-source model servers use simplified,
// lowercase names (e.g., `vllm`).
rpc FetchModelServers(FetchModelServersRequest)
returns (FetchModelServersResponse) {
option (google.api.http) = {
get: "/v1/modelServers:fetch"
};
}
// Fetches available model server versions. Open-source servers use their own
// versioning schemas (e.g., `vllm` uses semver like `v1.0.0`).
//
// Some model servers have different versioning schemas depending on the
// accelerator. For example, `vllm` uses semver on GPUs, but returns nightly
// build tags on TPUs. All available versions will be returned when different
// schemas are present.
rpc FetchModelServerVersions(FetchModelServerVersionsRequest)
returns (FetchModelServerVersionsResponse) {
option (google.api.http) = {
get: "/v1/modelServerVersions:fetch"
};
}
// Fetches available profiles. A profile contains performance metrics and
// cost information for a specific model server setup. Profiles can be
// filtered by parameters. If no filters are provided, all profiles are
// returned.
//
// Profiles display a single value per performance metric based on the
// provided performance requirements. If no requirements are given, the
// metrics represent the inflection point. See [Run best practice inference
// with GKE Inference Quickstart
// recipes](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/inference-quickstart#how)
// for details.
rpc FetchProfiles(FetchProfilesRequest) returns (FetchProfilesResponse) {
option (google.api.http) = {
post: "/v1/profiles:fetch"
body: "*"
};
}
// Generates an optimized deployment manifest for a given model and model
// server, based on the specified accelerator, performance targets, and
// configurations. See [Run best practice inference with GKE Inference
// Quickstart
// recipes](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/inference-quickstart)
// for deployment details.
rpc GenerateOptimizedManifest(GenerateOptimizedManifestRequest)
returns (GenerateOptimizedManifestResponse) {
option (google.api.http) = {
post: "/v1/optimizedManifest:generate"
body: "*"
};
}
// Fetches all of the benchmarking data available for a profile. Benchmarking
// data returns all of the performance metrics available for a given model
// server setup on a given instance type.
rpc FetchBenchmarkingData(FetchBenchmarkingDataRequest)
returns (FetchBenchmarkingDataResponse) {
option (google.api.http) = {
post: "/v1/benchmarkingData:fetch"
body: "*"
};
}
}
// Request message for
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels].
message FetchModelsRequest {
// Optional. The target number of results to return in a single response.
// If not specified, a default value will be chosen by the service.
// Note that the response may include a partial list and a caller should
// only rely on the response's
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelsResponse.next_page_token]
// to determine if there are more instances left to be queried.
optional int32 page_size = 1 [(google.api.field_behavior) = OPTIONAL];
// Optional. The value of
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelsResponse.next_page_token]
// received from a previous `FetchModelsRequest` call.
// Provide this to retrieve the subsequent page in a multi-page list of
// results. When paginating, all other parameters provided to
// `FetchModelsRequest` must match the call that provided the page token.
optional string page_token = 2 [(google.api.field_behavior) = OPTIONAL];
}
// Response message for
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels].
message FetchModelsResponse {
// Output only. List of available models. Open-source models follow the
// Huggingface Hub `owner/model_name` format.
repeated string models = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. A token which may be sent as
// [page_token][FetchModelsResponse.page_token] in a subsequent
// `FetchModelsResponse` call to retrieve the next page of results.
// If this field is omitted or empty, then there are no more results to
// return.
string next_page_token = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Request message for
// [GkeInferenceQuickstart.FetchModelServers][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServers].
message FetchModelServersRequest {
// Required. The model for which to list model servers. Open-source models
// follow the Huggingface Hub `owner/model_name` format. Use
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels]
// to find available models.
string model = 1 [(google.api.field_behavior) = REQUIRED];
// Optional. The target number of results to return in a single response.
// If not specified, a default value will be chosen by the service.
// Note that the response may include a partial list and a caller should
// only rely on the response's
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelServersResponse.next_page_token]
// to determine if there are more instances left to be queried.
optional int32 page_size = 2 [(google.api.field_behavior) = OPTIONAL];
// Optional. The value of
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelServersResponse.next_page_token]
// received from a previous `FetchModelServersRequest` call.
// Provide this to retrieve the subsequent page in a multi-page list of
// results. When paginating, all other parameters provided to
// `FetchModelServersRequest` must match the call that provided the page
// token.
optional string page_token = 3 [(google.api.field_behavior) = OPTIONAL];
}
// Response message for
// [GkeInferenceQuickstart.FetchModelServers][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServers].
message FetchModelServersResponse {
// Output only. List of available model servers. Open-source model servers use
// simplified, lowercase names (e.g., `vllm`).
repeated string model_servers = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. A token which may be sent as
// [page_token][FetchModelServersResponse.page_token] in a subsequent
// `FetchModelServersResponse` call to retrieve the next page of results.
// If this field is omitted or empty, then there are no more results to
// return.
string next_page_token = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Request message for
// [GkeInferenceQuickstart.FetchModelServerVersions][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServerVersions].
message FetchModelServerVersionsRequest {
// Required. The model for which to list model server versions. Open-source
// models follow the Huggingface Hub `owner/model_name` format. Use
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels]
// to find available models.
string model = 1 [(google.api.field_behavior) = REQUIRED];
// Required. The model server for which to list versions. Open-source model
// servers use simplified, lowercase names (e.g., `vllm`). Use
// [GkeInferenceQuickstart.FetchModelServers][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServers]
// to find available model servers.
string model_server = 2 [(google.api.field_behavior) = REQUIRED];
// Optional. The target number of results to return in a single response.
// If not specified, a default value will be chosen by the service.
// Note that the response may include a partial list and a caller should
// only rely on the response's
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelServerVersionsResponse.next_page_token]
// to determine if there are more instances left to be queried.
optional int32 page_size = 3 [(google.api.field_behavior) = OPTIONAL];
// Optional. The value of
// [next_page_token][google.cloud.gkerecommender.v1.FetchModelServerVersionsResponse.next_page_token]
// received from a previous `FetchModelServerVersionsRequest` call.
// Provide this to retrieve the subsequent page in a multi-page list of
// results. When paginating, all other parameters provided to
// `FetchModelServerVersionsRequest` must match the call that provided the
// page token.
optional string page_token = 4 [(google.api.field_behavior) = OPTIONAL];
}
// Response message for
// [GkeInferenceQuickstart.FetchModelServerVersions][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServerVersions].
message FetchModelServerVersionsResponse {
// Output only. A list of available model server versions.
repeated string model_server_versions = 1
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. A token which may be sent as
// [page_token][FetchModelServerVersionsResponse.page_token] in a subsequent
// `FetchModelServerVersionsResponse` call to retrieve the next page of
// results. If this field is omitted or empty, then there are no more results
// to return.
string next_page_token = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Request message for
// [GkeInferenceQuickstart.FetchBenchmarkingData][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchBenchmarkingData].
message FetchBenchmarkingDataRequest {
// Required. The model server configuration to get benchmarking data for. Use
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles]
// to find valid configurations.
ModelServerInfo model_server_info = 1
[(google.api.field_behavior) = REQUIRED];
// Optional. The instance type to filter benchmarking data. Instance types are
// in the format `a2-highgpu-1g`. If not provided, all instance types for the
// given profile's `model_server_info` will be returned. Use
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles]
// to find available instance types.
string instance_type = 3 [(google.api.field_behavior) = OPTIONAL];
// Optional. The pricing model to use for the benchmarking data. Defaults to
// `spot`.
string pricing_model = 4 [(google.api.field_behavior) = OPTIONAL];
}
// Response message for
// [GkeInferenceQuickstart.FetchBenchmarkingData][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchBenchmarkingData].
message FetchBenchmarkingDataResponse {
// Output only. List of profiles containing their respective benchmarking
// data.
repeated Profile profile = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Request message for
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles].
message FetchProfilesRequest {
// Optional. The model to filter profiles by. Open-source models follow the
// Huggingface Hub `owner/model_name` format. If not provided, all models are
// returned. Use
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels]
// to find available models.
string model = 1 [(google.api.field_behavior) = OPTIONAL];
// Optional. The model server to filter profiles by. If not provided, all
// model servers are returned. Use
// [GkeInferenceQuickstart.FetchModelServers][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServers]
// to find available model servers for a given model.
string model_server = 2 [(google.api.field_behavior) = OPTIONAL];
// Optional. The model server version to filter profiles by. If not provided,
// all model server versions are returned. Use
// [GkeInferenceQuickstart.FetchModelServerVersions][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServerVersions]
// to find available versions for a given model and server.
string model_server_version = 3 [(google.api.field_behavior) = OPTIONAL];
// Optional. The performance requirements to filter profiles. Profiles that do
// not meet these requirements are filtered out. If not provided, all profiles
// are returned.
PerformanceRequirements performance_requirements = 4
[(google.api.field_behavior) = OPTIONAL];
// Optional. The target number of results to return in a single response. If
// not specified, a default value will be chosen by the service. Note that the
// response may include a partial list and a caller should only rely on the
// response's
// [next_page_token][google.cloud.gkerecommender.v1.FetchProfilesResponse.next_page_token]
// to determine if there are more instances left to be queried.
optional int32 page_size = 5 [(google.api.field_behavior) = OPTIONAL];
// Optional. The value of
// [next_page_token][google.cloud.gkerecommender.v1.FetchProfilesResponse.next_page_token]
// received from a previous `FetchProfilesRequest` call.
// Provide this to retrieve the subsequent page in a multi-page list of
// results. When paginating, all other parameters provided to
// `FetchProfilesRequest` must match the call that provided the page
// token.
optional string page_token = 6 [(google.api.field_behavior) = OPTIONAL];
}
// Performance requirements for a profile and or model deployment.
message PerformanceRequirements {
// Optional. The target Normalized Time Per Output Token (NTPOT) in
// milliseconds. NTPOT is calculated as `request_latency /
// total_output_tokens`. If not provided, this target will not be enforced.
optional int32 target_ntpot_milliseconds = 1
[(google.api.field_behavior) = OPTIONAL];
// Optional. The target Time To First Token (TTFT) in milliseconds. TTFT is
// the time it takes to generate the first token for a request. If not
// provided, this target will not be enforced.
optional int32 target_ttft_milliseconds = 2
[(google.api.field_behavior) = OPTIONAL];
// Optional. The target cost for running a profile's model server. If not
// provided, this requirement will not be enforced.
Cost target_cost = 3 [(google.api.field_behavior) = OPTIONAL];
}
// Represents an amount of money in a specific currency.
message Amount {
// Output only. The whole units of the amount.
// For example if `currencyCode` is `"USD"`, then 1 unit is one US dollar.
int64 units = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. Number of nano (10^-9) units of the amount.
// The value must be between -999,999,999 and +999,999,999 inclusive.
// If `units` is positive, `nanos` must be positive or zero.
// If `units` is zero, `nanos` can be positive, zero, or negative.
// If `units` is negative, `nanos` must be negative or zero.
// For example $-1.75 is represented as `units`=-1 and `nanos`=-750,000,000.
int32 nanos = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Cost for running a model deployment on a given instance type. Currently, only
// USD currency code is supported.
message Cost {
// Optional. The cost per million output tokens, calculated as:
// $/output token = GPU $/s / (1/output-to-input-cost-ratio * input tokens/s +
// output tokens/s)
Amount cost_per_million_output_tokens = 1
[(google.api.field_behavior) = OPTIONAL];
// Optional. The cost per million input tokens. $/input token = ($/output
// token) / output-to-input-cost-ratio.
Amount cost_per_million_input_tokens = 2
[(google.api.field_behavior) = OPTIONAL];
// Optional. The pricing model used to calculate the cost. Can be one of:
// `3-years-cud`, `1-year-cud`, `on-demand`, `spot`. If not provided, `spot`
// will be used.
string pricing_model = 3 [(google.api.field_behavior) = OPTIONAL];
// Optional. The output-to-input cost ratio. This determines how the total GPU
// cost is split between input and output tokens. If not provided, `4.0` is
// used, assuming a 4:1 output:input cost ratio.
optional float output_input_cost_ratio = 4
[(google.api.field_behavior) = OPTIONAL];
}
// Represents a range of throughput values in tokens per second.
message TokensPerSecondRange {
// Output only. The minimum value of the range.
int32 min = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The maximum value of the range.
int32 max = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Represents a range of latency values in milliseconds.
message MillisecondRange {
// Output only. The minimum value of the range.
int32 min = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The maximum value of the range.
int32 max = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Performance range for a model deployment.
message PerformanceRange {
// Output only. The range of throughput in output tokens per second. This is
// measured as total_output_tokens_generated_by_server /
// elapsed_time_in_seconds.
TokensPerSecondRange throughput_output_range = 1
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The range of TTFT (Time To First Token) in milliseconds. TTFT
// is the time it takes to generate the first token for a request.
MillisecondRange ttft_range = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The range of NTPOT (Normalized Time Per Output Token) in
// milliseconds. NTPOT is the request latency normalized by the number of
// output tokens, measured as request_latency / total_output_tokens.
MillisecondRange ntpot_range = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Response message for
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles].
message FetchProfilesResponse {
// Output only. List of profiles that match the given model server info and
// performance requirements (if provided).
repeated Profile profile = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The combined range of performance values observed across all
// profiles in this response.
PerformanceRange performance_range = 2
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. Additional comments related to the response.
string comments = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. A token which may be sent as
// [page_token][FetchProfilesResponse.page_token] in a subsequent
// `FetchProfilesResponse` call to retrieve the next page of results. If this
// field is omitted or empty, then there are no more results to return.
string next_page_token = 4 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Model server information gives. Valid model server info combinations can
// be found using
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles].
message ModelServerInfo {
// Required. The model. Open-source models follow the Huggingface Hub
// `owner/model_name` format. Use
// [GkeInferenceQuickstart.FetchModels][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModels]
// to find available models.
string model = 1 [(google.api.field_behavior) = REQUIRED];
// Required. The model server. Open-source model servers use simplified,
// lowercase names (e.g., `vllm`). Use
// [GkeInferenceQuickstart.FetchModelServers][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServers]
// to find available servers.
string model_server = 2 [(google.api.field_behavior) = REQUIRED];
// Optional. The model server version. Use
// [GkeInferenceQuickstart.FetchModelServerVersions][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchModelServerVersions]
// to find available versions. If not provided, the latest available version
// is used.
string model_server_version = 3 [(google.api.field_behavior) = OPTIONAL];
}
// Resources used by a model deployment.
message ResourcesUsed {
// Output only. The number of accelerators (e.g., GPUs or TPUs) used by the
// model deployment on the Kubernetes node.
int32 accelerator_count = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Performance statistics for a model deployment.
message PerformanceStats {
// Output only. The number of queries per second.
// Note: This metric can vary widely based on context length and may not be a
// reliable measure of LLM throughput.
float queries_per_second = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The number of output tokens per second. This is the throughput
// measured as total_output_tokens_generated_by_server /
// elapsed_time_in_seconds.
int32 output_tokens_per_second = 2
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The Normalized Time Per Output Token (NTPOT) in milliseconds.
// This is the request latency normalized by the number of output tokens,
// measured as request_latency / total_output_tokens.
int32 ntpot_milliseconds = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The Time To First Token (TTFT) in milliseconds. This is the
// time it takes to generate the first token for a request.
int32 ttft_milliseconds = 4 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The cost of running the model deployment.
repeated Cost cost = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// A profile containing information about a model deployment.
message Profile {
// Output only. The model server configuration. Use
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles]
// to find valid configurations.
ModelServerInfo model_server_info = 1
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The accelerator type. Expected format: `nvidia-h100-80gb`.
string accelerator_type = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The TPU topology (if applicable).
string tpu_topology = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The instance type. Expected format: `a2-highgpu-1g`.
string instance_type = 4 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The resources used by the model deployment.
ResourcesUsed resources_used = 5 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. The performance statistics for this profile.
repeated PerformanceStats performance_stats = 6
[(google.api.field_behavior) = OUTPUT_ONLY];
}
// Request message for
// [GkeInferenceQuickstart.GenerateOptimizedManifest][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.GenerateOptimizedManifest].
message GenerateOptimizedManifestRequest {
// Required. The model server configuration to generate the manifest for. Use
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles]
// to find valid configurations.
ModelServerInfo model_server_info = 1
[(google.api.field_behavior) = REQUIRED];
// Required. The accelerator type. Use
// [GkeInferenceQuickstart.FetchProfiles][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.FetchProfiles]
// to find valid accelerators for a given `model_server_info`.
string accelerator_type = 2 [(google.api.field_behavior) = REQUIRED];
// Optional. The kubernetes namespace to deploy the manifests in.
string kubernetes_namespace = 3 [(google.api.field_behavior) = OPTIONAL];
// Optional. The performance requirements to use for generating Horizontal Pod
// Autoscaler (HPA) resources. If provided, the manifest includes HPA
// resources to adjust the model server replica count to maintain the
// specified targets (e.g., NTPOT, TTFT) at a P50 latency. Cost targets are
// not currently supported for HPA generation. If the specified targets are
// not achievable, the HPA manifest will not be generated.
PerformanceRequirements performance_requirements = 4
[(google.api.field_behavior) = OPTIONAL];
// Optional. The storage configuration for the model. If not provided, the
// model is loaded from Huggingface.
StorageConfig storage_config = 5 [(google.api.field_behavior) = OPTIONAL];
}
// A Kubernetes manifest.
message KubernetesManifest {
// Output only. Kubernetes resource kind.
string kind = 1 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. Kubernetes API version.
string api_version = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. YAML content.
string content = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Response message for
// [GkeInferenceQuickstart.GenerateOptimizedManifest][google.cloud.gkerecommender.v1.GkeInferenceQuickstart.GenerateOptimizedManifest].
message GenerateOptimizedManifestResponse {
// Output only. A list of generated Kubernetes manifests.
repeated KubernetesManifest kubernetes_manifests = 1
[(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. Comments related to deploying the generated manifests.
repeated string comments = 2 [(google.api.field_behavior) = OUTPUT_ONLY];
// Output only. Additional information about the versioned dependencies used
// to generate the manifests. See [Run best practice inference with GKE
// Inference Quickstart
// recipes](https://cloud.google.com/kubernetes-engine/docs/how-to/machine-learning/inference/inference-quickstart)
// for details.
string manifest_version = 3 [(google.api.field_behavior) = OUTPUT_ONLY];
}
// Storage configuration for a model deployment.
message StorageConfig {
// Optional. The Google Cloud Storage bucket URI to load the model from. This
// URI must point to the directory containing the model's config file
// (`config.json`) and model weights. A tuned GCSFuse setup can improve
// LLM Pod startup time by more than 7x. Expected format:
// `gs://<bucket-name>/<path-to-model>`.
string model_bucket_uri = 1 [(google.api.field_behavior) = OPTIONAL];
// Optional. The URI for the GCS bucket containing the XLA compilation cache.
// If using TPUs, the XLA cache will be written to the same path as
// `model_bucket_uri`. This can speed up vLLM model preparation for repeated
// deployments.
string xla_cache_bucket_uri = 2 [(google.api.field_behavior) = OPTIONAL];
}

15
google/cloud/gkerecommender/v1/gkerecommender_grpc_service_config.json Normal file
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@ -0,0 +1,15 @@
{
"methodConfig": [
{
"name": [
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "FetchModels" },
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "FetchModelServers" },
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "FetchModelServerVersions" },
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "FetchProfiles" },
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "GenerateOptimizedManifest" },
{ "service": "google.cloud.gkerecommender.v1.GkeInferenceQuickstart", "method": "FetchBenchmarkingData" }
],
"timeout": "60s"
}
]
}

17
google/cloud/gkerecommender/v1/gkerecommender_v1.yaml Normal file
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@ -0,0 +1,17 @@
type: google.api.Service
config_version: 3
name: gkerecommender.googleapis.com
title: GKE Recommender API
apis:
- name: google.cloud.gkerecommender.v1.GkeInferenceQuickstart
documentation:
summary: GKE Recommender API
authentication:
rules:
- selector: 'google.cloud.gkerecommender.v1.GkeInferenceQuickstart.*'
oauth:
canonical_scopes: |-
https://www.googleapis.com/auth/cloud-platform