--- /dev/null
+// Copyright 2017 Google Inc.
+//
+// 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.ml.v1;
+
+import "google/api/annotations.proto";
+import "google/api/auth.proto";
+import "google/protobuf/empty.proto";
+import "google/protobuf/timestamp.proto";
+
+option go_package = "google.golang.org/genproto/googleapis/cloud/ml/v1;ml";
+option java_multiple_files = true;
+option java_outer_classname = "JobServiceProto";
+option java_package = "com.google.cloud.ml.api.v1";
+
+// Copyright 2017 Google Inc. All Rights Reserved.
+//
+// Proto file for the Google Cloud Machine Learning Engine.
+// Describes the 'job service' to manage training and prediction jobs.
+
+// Service to create and manage training and batch prediction jobs.
+service JobService {
+ // Creates a training or a batch prediction job.
+ rpc CreateJob(CreateJobRequest) returns (Job) {
+ option (google.api.http) = {
+ post: "/v1/{parent=projects/*}/jobs"
+ body: "job"
+ };
+ }
+
+ // Lists the jobs in the project.
+ rpc ListJobs(ListJobsRequest) returns (ListJobsResponse) {
+ option (google.api.http) = {
+ get: "/v1/{parent=projects/*}/jobs"
+ };
+ }
+
+ // Describes a job.
+ rpc GetJob(GetJobRequest) returns (Job) {
+ option (google.api.http) = {
+ get: "/v1/{name=projects/*/jobs/*}"
+ };
+ }
+
+ // Cancels a running job.
+ rpc CancelJob(CancelJobRequest) returns (google.protobuf.Empty) {
+ option (google.api.http) = {
+ post: "/v1/{name=projects/*/jobs/*}:cancel"
+ body: "*"
+ };
+ }
+}
+
+// Represents input parameters for a training job.
+message TrainingInput {
+ // A scale tier is an abstract representation of the resources Cloud ML
+ // will allocate to a training job. When selecting a scale tier for your
+ // training job, you should consider the size of your training dataset and
+ // the complexity of your model. As the tiers increase, virtual machines are
+ // added to handle your job, and the individual machines in the cluster
+ // generally have more memory and greater processing power than they do at
+ // lower tiers. The number of training units charged per hour of processing
+ // increases as tiers get more advanced. Refer to the
+ // [pricing guide](/ml/pricing) for more details. Note that in addition to
+ // incurring costs, your use of training resources is constrained by the
+ // [quota policy](/ml/quota).
+ enum ScaleTier {
+ // A single worker instance. This tier is suitable for learning how to use
+ // Cloud ML, and for experimenting with new models using small datasets.
+ BASIC = 0;
+
+ // Many workers and a few parameter servers.
+ STANDARD_1 = 1;
+
+ // A large number of workers with many parameter servers.
+ PREMIUM_1 = 3;
+
+ // A single worker instance [with a GPU](ml/docs/how-tos/using-gpus).
+ BASIC_GPU = 6;
+
+ // The CUSTOM tier is not a set tier, but rather enables you to use your
+ // own cluster specification. When you use this tier, set values to
+ // configure your processing cluster according to these guidelines:
+ //
+ // * You _must_ set `TrainingInput.masterType` to specify the type
+ // of machine to use for your master node. This is the only required
+ // setting.
+ //
+ // * You _may_ set `TrainingInput.workerCount` to specify the number of
+ // workers to use. If you specify one or more workers, you _must_ also
+ // set `TrainingInput.workerType` to specify the type of machine to use
+ // for your worker nodes.
+ //
+ // * You _may_ set `TrainingInput.parameterServerCount` to specify the
+ // number of parameter servers to use. If you specify one or more
+ // parameter servers, you _must_ also set
+ // `TrainingInput.parameterServerType` to specify the type of machine to
+ // use for your parameter servers.
+ //
+ // Note that all of your workers must use the same machine type, which can
+ // be different from your parameter server type and master type. Your
+ // parameter servers must likewise use the same machine type, which can be
+ // different from your worker type and master type.
+ CUSTOM = 5;
+ }
+
+ // Required. Specifies the machine types, the number of replicas for workers
+ // and parameter servers.
+ ScaleTier scale_tier = 1;
+
+ // Optional. Specifies the type of virtual machine to use for your training
+ // job's master worker.
+ //
+ // The following types are supported:
+ //
+ // <dl>
+ // <dt>standard</dt>
+ // <dd>
+ // A basic machine configuration suitable for training simple models with
+ // small to moderate datasets.
+ // </dd>
+ // <dt>large_model</dt>
+ // <dd>
+ // A machine with a lot of memory, specially suited for parameter servers
+ // when your model is large (having many hidden layers or layers with very
+ // large numbers of nodes).
+ // </dd>
+ // <dt>complex_model_s</dt>
+ // <dd>
+ // A machine suitable for the master and workers of the cluster when your
+ // model requires more computation than the standard machine can handle
+ // satisfactorily.
+ // </dd>
+ // <dt>complex_model_m</dt>
+ // <dd>
+ // A machine with roughly twice the number of cores and roughly double the
+ // memory of <code suppresswarning="true">complex_model_s</code>.
+ // </dd>
+ // <dt>complex_model_l</dt>
+ // <dd>
+ // A machine with roughly twice the number of cores and roughly double the
+ // memory of <code suppresswarning="true">complex_model_m</code>.
+ // </dd>
+ // <dt>standard_gpu</dt>
+ // <dd>
+ // A machine equivalent to <code suppresswarning="true">standard</code> that
+ // also includes a
+ // <a href="ml/docs/how-tos/using-gpus">
+ // GPU that you can use in your trainer</a>.
+ // </dd>
+ // <dt>complex_model_m_gpu</dt>
+ // <dd>
+ // A machine equivalent to
+ // <code suppresswarning="true">coplex_model_m</code> that also includes
+ // four GPUs.
+ // </dd>
+ // </dl>
+ //
+ // You must set this value when `scaleTier` is set to `CUSTOM`.
+ string master_type = 2;
+
+ // Optional. Specifies the type of virtual machine to use for your training
+ // job's worker nodes.
+ //
+ // The supported values are the same as those described in the entry for
+ // `masterType`.
+ //
+ // This value must be present when `scaleTier` is set to `CUSTOM` and
+ // `workerCount` is greater than zero.
+ string worker_type = 3;
+
+ // Optional. Specifies the type of virtual machine to use for your training
+ // job's parameter server.
+ //
+ // The supported values are the same as those described in the entry for
+ // `master_type`.
+ //
+ // This value must be present when `scaleTier` is set to `CUSTOM` and
+ // `parameter_server_count` is greater than zero.
+ string parameter_server_type = 4;
+
+ // Optional. The number of worker replicas to use for the training job. Each
+ // replica in the cluster will be of the type specified in `worker_type`.
+ //
+ // This value can only be used when `scale_tier` is set to `CUSTOM`. If you
+ // set this value, you must also set `worker_type`.
+ int64 worker_count = 5;
+
+ // Optional. The number of parameter server replicas to use for the training
+ // job. Each replica in the cluster will be of the type specified in
+ // `parameter_server_type`.
+ //
+ // This value can only be used when `scale_tier` is set to `CUSTOM`.If you
+ // set this value, you must also set `parameter_server_type`.
+ int64 parameter_server_count = 6;
+
+ // Required. The Google Cloud Storage location of the packages with
+ // the training program and any additional dependencies.
+ repeated string package_uris = 7;
+
+ // Required. The Python module name to run after installing the packages.
+ string python_module = 8;
+
+ // Optional. Command line arguments to pass to the program.
+ repeated string args = 10;
+
+ // Optional. The set of Hyperparameters to tune.
+ HyperparameterSpec hyperparameters = 12;
+
+ // Required. The Google Compute Engine region to run the training job in.
+ string region = 14;
+
+ // Optional. A Google Cloud Storage path in which to store training outputs
+ // and other data needed for training. This path is passed to your TensorFlow
+ // program as the 'job_dir' command-line argument. The benefit of specifying
+ // this field is that Cloud ML validates the path for use in training.
+ string job_dir = 16;
+
+ // Optional. The Google Cloud ML runtime version to use for training. If not
+ // set, Google Cloud ML will choose the latest stable version.
+ string runtime_version = 15;
+}
+
+// Represents a set of hyperparameters to optimize.
+message HyperparameterSpec {
+ // The available types of optimization goals.
+ enum GoalType {
+ // Goal Type will default to maximize.
+ GOAL_TYPE_UNSPECIFIED = 0;
+
+ // Maximize the goal metric.
+ MAXIMIZE = 1;
+
+ // Minimize the goal metric.
+ MINIMIZE = 2;
+ }
+
+ // Required. The type of goal to use for tuning. Available types are
+ // `MAXIMIZE` and `MINIMIZE`.
+ //
+ // Defaults to `MAXIMIZE`.
+ GoalType goal = 1;
+
+ // Required. The set of parameters to tune.
+ repeated ParameterSpec params = 2;
+
+ // Optional. How many training trials should be attempted to optimize
+ // the specified hyperparameters.
+ //
+ // Defaults to one.
+ int32 max_trials = 3;
+
+ // Optional. The number of training trials to run concurrently.
+ // You can reduce the time it takes to perform hyperparameter tuning by adding
+ // trials in parallel. However, each trail only benefits from the information
+ // gained in completed trials. That means that a trial does not get access to
+ // the results of trials running at the same time, which could reduce the
+ // quality of the overall optimization.
+ //
+ // Each trial will use the same scale tier and machine types.
+ //
+ // Defaults to one.
+ int32 max_parallel_trials = 4;
+
+ // Optional. The Tensorflow summary tag name to use for optimizing trials. For
+ // current versions of Tensorflow, this tag name should exactly match what is
+ // shown in Tensorboard, including all scopes. For versions of Tensorflow
+ // prior to 0.12, this should be only the tag passed to tf.Summary.
+ // By default, "training/hptuning/metric" will be used.
+ string hyperparameter_metric_tag = 5;
+}
+
+// Represents a single hyperparameter to optimize.
+message ParameterSpec {
+ // The type of the parameter.
+ enum ParameterType {
+ // You must specify a valid type. Using this unspecified type will result in
+ // an error.
+ PARAMETER_TYPE_UNSPECIFIED = 0;
+
+ // Type for real-valued parameters.
+ DOUBLE = 1;
+
+ // Type for integral parameters.
+ INTEGER = 2;
+
+ // The parameter is categorical, with a value chosen from the categories
+ // field.
+ CATEGORICAL = 3;
+
+ // The parameter is real valued, with a fixed set of feasible points. If
+ // `type==DISCRETE`, feasible_points must be provided, and
+ // {`min_value`, `max_value`} will be ignored.
+ DISCRETE = 4;
+ }
+
+ // The type of scaling that should be applied to this parameter.
+ enum ScaleType {
+ // By default, no scaling is applied.
+ NONE = 0;
+
+ // Scales the feasible space to (0, 1) linearly.
+ UNIT_LINEAR_SCALE = 1;
+
+ // Scales the feasible space logarithmically to (0, 1). The entire feasible
+ // space must be strictly positive.
+ UNIT_LOG_SCALE = 2;
+
+ // Scales the feasible space "reverse" logarithmically to (0, 1). The result
+ // is that values close to the top of the feasible space are spread out more
+ // than points near the bottom. The entire feasible space must be strictly
+ // positive.
+ UNIT_REVERSE_LOG_SCALE = 3;
+ }
+
+ // Required. The parameter name must be unique amongst all ParameterConfigs in
+ // a HyperparameterSpec message. E.g., "learning_rate".
+ string parameter_name = 1;
+
+ // Required. The type of the parameter.
+ ParameterType type = 4;
+
+ // Required if type is `DOUBLE` or `INTEGER`. This field
+ // should be unset if type is `CATEGORICAL`. This value should be integers if
+ // type is INTEGER.
+ double min_value = 2;
+
+ // Required if typeis `DOUBLE` or `INTEGER`. This field
+ // should be unset if type is `CATEGORICAL`. This value should be integers if
+ // type is `INTEGER`.
+ double max_value = 3;
+
+ // Required if type is `CATEGORICAL`. The list of possible categories.
+ repeated string categorical_values = 5;
+
+ // Required if type is `DISCRETE`.
+ // A list of feasible points.
+ // The list should be in strictly increasing order. For instance, this
+ // parameter might have possible settings of 1.5, 2.5, and 4.0. This list
+ // should not contain more than 1,000 values.
+ repeated double discrete_values = 6;
+
+ // Optional. How the parameter should be scaled to the hypercube.
+ // Leave unset for categorical parameters.
+ // Some kind of scaling is strongly recommended for real or integral
+ // parameters (e.g., `UNIT_LINEAR_SCALE`).
+ ScaleType scale_type = 7;
+}
+
+// Represents the result of a single hyperparameter tuning trial from a
+// training job. The TrainingOutput object that is returned on successful
+// completion of a training job with hyperparameter tuning includes a list
+// of HyperparameterOutput objects, one for each successful trial.
+message HyperparameterOutput {
+ // An observed value of a metric.
+ message HyperparameterMetric {
+ // The global training step for this metric.
+ int64 training_step = 1;
+
+ // The objective value at this training step.
+ double objective_value = 2;
+ }
+
+ // The trial id for these results.
+ string trial_id = 1;
+
+ // The hyperparameters given to this trial.
+ map<string, string> hyperparameters = 2;
+
+ // The final objective metric seen for this trial.
+ HyperparameterMetric final_metric = 3;
+
+ // All recorded object metrics for this trial.
+ repeated HyperparameterMetric all_metrics = 4;
+}
+
+// Represents results of a training job. Output only.
+message TrainingOutput {
+ // The number of hyperparameter tuning trials that completed successfully.
+ // Only set for hyperparameter tuning jobs.
+ int64 completed_trial_count = 1;
+
+ // Results for individual Hyperparameter trials.
+ // Only set for hyperparameter tuning jobs.
+ repeated HyperparameterOutput trials = 2;
+
+ // The amount of ML units consumed by the job.
+ double consumed_ml_units = 3;
+
+ // Whether this job is a hyperparameter tuning job.
+ bool is_hyperparameter_tuning_job = 4;
+}
+
+// Represents input parameters for a prediction job.
+message PredictionInput {
+ // The format used to separate data instances in the source files.
+ enum DataFormat {
+ // Unspecified format.
+ DATA_FORMAT_UNSPECIFIED = 0;
+
+ // The source file is a text file with instances separated by the
+ // new-line character.
+ TEXT = 1;
+
+ // The source file is a TFRecord file.
+ TF_RECORD = 2;
+
+ // The source file is a GZIP-compressed TFRecord file.
+ TF_RECORD_GZIP = 3;
+ }
+
+ // Required. The model or the version to use for prediction.
+ oneof model_version {
+ // Use this field if you want to use the default version for the specified
+ // model. The string must use the following format:
+ //
+ // `"projects/<var>[YOUR_PROJECT]</var>/models/<var>[YOUR_MODEL]</var>"`
+ string model_name = 1;
+
+ // Use this field if you want to specify a version of the model to use. The
+ // string is formatted the same way as `model_version`, with the addition
+ // of the version information:
+ //
+ // `"projects/<var>[YOUR_PROJECT]</var>/models/<var>YOUR_MODEL/versions/<var>[YOUR_VERSION]</var>"`
+ string version_name = 2;
+
+ // Use this field if you want to specify a Google Cloud Storage path for
+ // the model to use.
+ string uri = 9;
+ }
+
+ // Required. The format of the input data files.
+ DataFormat data_format = 3;
+
+ // Required. The Google Cloud Storage location of the input data files.
+ // May contain wildcards.
+ repeated string input_paths = 4;
+
+ // Required. The output Google Cloud Storage location.
+ string output_path = 5;
+
+ // Optional. The maximum number of workers to be used for parallel processing.
+ // Defaults to 10 if not specified.
+ int64 max_worker_count = 6;
+
+ // Required. The Google Compute Engine region to run the prediction job in.
+ string region = 7;
+
+ // Optional. The Google Cloud ML runtime version to use for this batch
+ // prediction. If not set, Google Cloud ML will pick the runtime version used
+ // during the CreateVersion request for this model version, or choose the
+ // latest stable version when model version information is not available
+ // such as when the model is specified by uri.
+ string runtime_version = 8;
+}
+
+// Represents results of a prediction job.
+message PredictionOutput {
+ // The output Google Cloud Storage location provided at the job creation time.
+ string output_path = 1;
+
+ // The number of generated predictions.
+ int64 prediction_count = 2;
+
+ // The number of data instances which resulted in errors.
+ int64 error_count = 3;
+
+ // Node hours used by the batch prediction job.
+ double node_hours = 4;
+}
+
+// Represents a training or prediction job.
+message Job {
+ // Describes the job state.
+ enum State {
+ // The job state is unspecified.
+ STATE_UNSPECIFIED = 0;
+
+ // The job has been just created and processing has not yet begun.
+ QUEUED = 1;
+
+ // The service is preparing to run the job.
+ PREPARING = 2;
+
+ // The job is in progress.
+ RUNNING = 3;
+
+ // The job completed successfully.
+ SUCCEEDED = 4;
+
+ // The job failed.
+ // `error_message` should contain the details of the failure.
+ FAILED = 5;
+
+ // The job is being cancelled.
+ // `error_message` should describe the reason for the cancellation.
+ CANCELLING = 6;
+
+ // The job has been cancelled.
+ // `error_message` should describe the reason for the cancellation.
+ CANCELLED = 7;
+ }
+
+ // Required. The user-specified id of the job.
+ string job_id = 1;
+
+ // Required. Parameters to create a job.
+ oneof input {
+ // Input parameters to create a training job.
+ TrainingInput training_input = 2;
+
+ // Input parameters to create a prediction job.
+ PredictionInput prediction_input = 3;
+ }
+
+ // Output only. When the job was created.
+ google.protobuf.Timestamp create_time = 4;
+
+ // Output only. When the job processing was started.
+ google.protobuf.Timestamp start_time = 5;
+
+ // Output only. When the job processing was completed.
+ google.protobuf.Timestamp end_time = 6;
+
+ // Output only. The detailed state of a job.
+ State state = 7;
+
+ // Output only. The details of a failure or a cancellation.
+ string error_message = 8;
+
+ // Output only. The current result of the job.
+ oneof output {
+ // The current training job result.
+ TrainingOutput training_output = 9;
+
+ // The current prediction job result.
+ PredictionOutput prediction_output = 10;
+ }
+}
+
+// Request message for the CreateJob method.
+message CreateJobRequest {
+ // Required. The project name.
+ //
+ // Authorization: requires `Editor` role on the specified project.
+ string parent = 1;
+
+ // Required. The job to create.
+ Job job = 2;
+}
+
+// Request message for the ListJobs method.
+message ListJobsRequest {
+ // Required. The name of the project for which to list jobs.
+ //
+ // Authorization: requires `Viewer` role on the specified project.
+ string parent = 1;
+
+ // Optional. Specifies the subset of jobs to retrieve.
+ string filter = 2;
+
+ // Optional. A page token to request the next page of results.
+ //
+ // You get the token from the `next_page_token` field of the response from
+ // the previous call.
+ string page_token = 4;
+
+ // Optional. The number of jobs to retrieve per "page" of results. If there
+ // are more remaining results than this number, the response message will
+ // contain a valid value in the `next_page_token` field.
+ //
+ // The default value is 20, and the maximum page size is 100.
+ int32 page_size = 5;
+}
+
+// Response message for the ListJobs method.
+message ListJobsResponse {
+ // The list of jobs.
+ repeated Job jobs = 1;
+
+ // Optional. Pass this token as the `page_token` field of the request for a
+ // subsequent call.
+ string next_page_token = 2;
+}
+
+// Request message for the GetJob method.
+message GetJobRequest {
+ // Required. The name of the job to get the description of.
+ //
+ // Authorization: requires `Viewer` role on the parent project.
+ string name = 1;
+}
+
+// Request message for the CancelJob method.
+message CancelJobRequest {
+ // Required. The name of the job to cancel.
+ //
+ // Authorization: requires `Editor` role on the parent project.
+ string name = 1;
+}
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