// 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.aiplatform.v1beta1;

import "google/api/field_behavior.proto";
import "google/api/resource.proto";
import "google/cloud/aiplatform/v1beta1/encryption_spec.proto";
import "google/cloud/aiplatform/v1beta1/env_var.proto";
import "google/cloud/aiplatform/v1beta1/io.proto";
import "google/cloud/aiplatform/v1beta1/job_state.proto";
import "google/cloud/aiplatform/v1beta1/machine_resources.proto";
import "google/cloud/aiplatform/v1beta1/service_networking.proto";
import "google/protobuf/duration.proto";
import "google/protobuf/timestamp.proto";
import "google/rpc/status.proto";

option csharp_namespace = "Google.Cloud.AIPlatform.V1Beta1";
option go_package = "cloud.google.com/go/aiplatform/apiv1beta1/aiplatformpb;aiplatformpb";
option java_multiple_files = true;
option java_outer_classname = "CustomJobProto";
option java_package = "com.google.cloud.aiplatform.v1beta1";
option php_namespace = "Google\\Cloud\\AIPlatform\\V1beta1";
option ruby_package = "Google::Cloud::AIPlatform::V1beta1";

// Represents a job that runs custom workloads such as a Docker container or a
// Python package. A CustomJob can have multiple worker pools and each worker
// pool can have its own machine and input spec. A CustomJob will be cleaned up
// once the job enters terminal state (failed or succeeded).
message CustomJob {
  option (google.api.resource) = {
    type: "aiplatform.googleapis.com/CustomJob"
    pattern: "projects/{project}/locations/{location}/customJobs/{custom_job}"
  };

  // Output only. Resource name of a CustomJob.
  string name = 1 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Required. The display name of the CustomJob.
  // The name can be up to 128 characters long and can consist of any UTF-8
  // characters.
  string display_name = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. Job spec.
  CustomJobSpec job_spec = 4 [(google.api.field_behavior) = REQUIRED];

  // Output only. The detailed state of the job.
  JobState state = 5 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Time when the CustomJob was created.
  google.protobuf.Timestamp create_time = 6
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Time when the CustomJob for the first time entered the
  // `JOB_STATE_RUNNING` state.
  google.protobuf.Timestamp start_time = 7
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Time when the CustomJob entered any of the following states:
  // `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.
  google.protobuf.Timestamp end_time = 8
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Time when the CustomJob was most recently updated.
  google.protobuf.Timestamp update_time = 9
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Only populated when job's state is `JOB_STATE_FAILED` or
  // `JOB_STATE_CANCELLED`.
  google.rpc.Status error = 10 [(google.api.field_behavior) = OUTPUT_ONLY];

  // The labels with user-defined metadata to organize CustomJobs.
  //
  // Label keys and values can be no longer than 64 characters
  // (Unicode codepoints), can only contain lowercase letters, numeric
  // characters, underscores and dashes. International characters are allowed.
  //
  // See https://goo.gl/xmQnxf for more information and examples of labels.
  map<string, string> labels = 11;

  // Customer-managed encryption key options for a CustomJob. If this is set,
  // then all resources created by the CustomJob will be encrypted with the
  // provided encryption key.
  EncryptionSpec encryption_spec = 12;

  // Output only. URIs for accessing [interactive
  // shells](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell)
  // (one URI for each training node). Only available if
  // [job_spec.enable_web_access][google.cloud.aiplatform.v1beta1.CustomJobSpec.enable_web_access]
  // is `true`.
  //
  // The keys are names of each node in the training job; for example,
  // `workerpool0-0` for the primary node, `workerpool1-0` for the first node in
  // the second worker pool, and `workerpool1-1` for the second node in the
  // second worker pool.
  //
  // The values are the URIs for each node's interactive shell.
  map<string, string> web_access_uris = 16
      [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Reserved for future use.
  bool satisfies_pzs = 18 [(google.api.field_behavior) = OUTPUT_ONLY];

  // Output only. Reserved for future use.
  bool satisfies_pzi = 19 [(google.api.field_behavior) = OUTPUT_ONLY];
}

// Represents the spec of a CustomJob.
message CustomJobSpec {
  // Optional. The ID of the PersistentResource in the same Project and Location
  // which to run
  //
  // If this is specified, the job will be run on existing machines held by the
  // PersistentResource instead of on-demand short-live machines.
  // The network and CMEK configs on the job should be consistent with those on
  // the PersistentResource, otherwise, the job will be rejected.
  string persistent_resource_id = 14 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/PersistentResource"
    }
  ];

  // Required. The spec of the worker pools including machine type and Docker
  // image. All worker pools except the first one are optional and can be
  // skipped by providing an empty value.
  repeated WorkerPoolSpec worker_pool_specs = 1
      [(google.api.field_behavior) = REQUIRED];

  // Scheduling options for a CustomJob.
  Scheduling scheduling = 3;

  // Specifies the service account for workload run-as account.
  // Users submitting jobs must have act-as permission on this run-as account.
  // If unspecified, the [Vertex AI Custom Code Service
  // Agent](https://cloud.google.com/vertex-ai/docs/general/access-control#service-agents)
  // for the CustomJob's project is used.
  string service_account = 4;

  // Optional. The full name of the Compute Engine
  // [network](/compute/docs/networks-and-firewalls#networks) to which the Job
  // should be peered. For example, `projects/12345/global/networks/myVPC`.
  // [Format](/compute/docs/reference/rest/v1/networks/insert)
  // is of the form `projects/{project}/global/networks/{network}`.
  // Where {project} is a project number, as in `12345`, and {network} is a
  // network name.
  //
  // To specify this field, you must have already [configured VPC Network
  // Peering for Vertex
  // AI](https://cloud.google.com/vertex-ai/docs/general/vpc-peering).
  //
  // If this field is left unspecified, the job is not peered with any network.
  string network = 5 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = { type: "compute.googleapis.com/Network" }
  ];

  // Optional. A list of names for the reserved ip ranges under the VPC network
  // that can be used for this job.
  //
  // If set, we will deploy the job within the provided ip ranges. Otherwise,
  // the job will be deployed to any ip ranges under the provided VPC
  // network.
  //
  // Example: ['vertex-ai-ip-range'].
  repeated string reserved_ip_ranges = 13
      [(google.api.field_behavior) = OPTIONAL];

  // Optional. Configuration for PSC-I for CustomJob.
  PscInterfaceConfig psc_interface_config = 21
      [(google.api.field_behavior) = OPTIONAL];

  // The Cloud Storage location to store the output of this CustomJob or
  // HyperparameterTuningJob. For HyperparameterTuningJob,
  // the baseOutputDirectory of
  // each child CustomJob backing a Trial is set to a subdirectory of name
  // [id][google.cloud.aiplatform.v1beta1.Trial.id] under its parent
  // HyperparameterTuningJob's baseOutputDirectory.
  //
  // The following Vertex AI environment variables will be passed to
  // containers or python modules when this field is set:
  //
  //   For CustomJob:
  //
  //   * AIP_MODEL_DIR = `<base_output_directory>/model/`
  //   * AIP_CHECKPOINT_DIR = `<base_output_directory>/checkpoints/`
  //   * AIP_TENSORBOARD_LOG_DIR = `<base_output_directory>/logs/`
  //
  //   For CustomJob backing a Trial of HyperparameterTuningJob:
  //
  //   * AIP_MODEL_DIR = `<base_output_directory>/<trial_id>/model/`
  //   * AIP_CHECKPOINT_DIR = `<base_output_directory>/<trial_id>/checkpoints/`
  //   * AIP_TENSORBOARD_LOG_DIR = `<base_output_directory>/<trial_id>/logs/`
  GcsDestination base_output_directory = 6;

  // The ID of the location to store protected artifacts. e.g. us-central1.
  // Populate only when the location is different than CustomJob location.
  // List of supported locations:
  // https://cloud.google.com/vertex-ai/docs/general/locations
  string protected_artifact_location_id = 19;

  // Optional. The name of a Vertex AI
  // [Tensorboard][google.cloud.aiplatform.v1beta1.Tensorboard] resource to
  // which this CustomJob will upload Tensorboard logs. Format:
  // `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
  string tensorboard = 7 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/Tensorboard"
    }
  ];

  // Optional. Whether you want Vertex AI to enable [interactive shell
  // access](https://cloud.google.com/vertex-ai/docs/training/monitor-debug-interactive-shell)
  // to training containers.
  //
  // If set to `true`, you can access interactive shells at the URIs given
  // by
  // [CustomJob.web_access_uris][google.cloud.aiplatform.v1beta1.CustomJob.web_access_uris]
  // or
  // [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris]
  // (within
  // [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.HyperparameterTuningJob.trials]).
  bool enable_web_access = 10 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Whether you want Vertex AI to enable access to the customized
  // dashboard in training chief container.
  //
  // If set to `true`, you can access the dashboard at the URIs given
  // by
  // [CustomJob.web_access_uris][google.cloud.aiplatform.v1beta1.CustomJob.web_access_uris]
  // or
  // [Trial.web_access_uris][google.cloud.aiplatform.v1beta1.Trial.web_access_uris]
  // (within
  // [HyperparameterTuningJob.trials][google.cloud.aiplatform.v1beta1.HyperparameterTuningJob.trials]).
  bool enable_dashboard_access = 16 [(google.api.field_behavior) = OPTIONAL];

  // Optional. The Experiment associated with this job.
  // Format:
  // `projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}`
  string experiment = 17 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/Context"
    }
  ];

  // Optional. The Experiment Run associated with this job.
  // Format:
  // `projects/{project}/locations/{location}/metadataStores/{metadataStores}/contexts/{experiment-name}-{experiment-run-name}`
  string experiment_run = 18 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/Context"
    }
  ];

  // Optional. The name of the Model resources for which to generate a mapping
  // to artifact URIs. Applicable only to some of the Google-provided custom
  // jobs. Format: `projects/{project}/locations/{location}/models/{model}`
  //
  // In order to retrieve a specific version of the model, also provide
  // the version ID or version alias.
  //   Example: `projects/{project}/locations/{location}/models/{model}@2`
  //              or
  //            `projects/{project}/locations/{location}/models/{model}@golden`
  // If no version ID or alias is specified, the "default" version will be
  // returned. The "default" version alias is created for the first version of
  // the model, and can be moved to other versions later on. There will be
  // exactly one default version.
  repeated string models = 20 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.resource_reference) = {
      type: "aiplatform.googleapis.com/Model"
    }
  ];
}

// Represents the spec of a worker pool in a job.
message WorkerPoolSpec {
  // The custom task to be executed in this worker pool.
  oneof task {
    // The custom container task.
    ContainerSpec container_spec = 6;

    // The Python packaged task.
    PythonPackageSpec python_package_spec = 7;
  }

  // Optional. Immutable. The specification of a single machine.
  MachineSpec machine_spec = 1 [
    (google.api.field_behavior) = OPTIONAL,
    (google.api.field_behavior) = IMMUTABLE
  ];

  // Optional. The number of worker replicas to use for this worker pool.
  int64 replica_count = 2 [(google.api.field_behavior) = OPTIONAL];

  // Optional. List of NFS mount spec.
  repeated NfsMount nfs_mounts = 4 [(google.api.field_behavior) = OPTIONAL];

  // Disk spec.
  DiskSpec disk_spec = 5;
}

// The spec of a Container.
message ContainerSpec {
  // Required. The URI of a container image in the Container Registry that is to
  // be run on each worker replica.
  string image_uri = 1 [(google.api.field_behavior) = REQUIRED];

  // The command to be invoked when the container is started.
  // It overrides the entrypoint instruction in Dockerfile when provided.
  repeated string command = 2;

  // The arguments to be passed when starting the container.
  repeated string args = 3;

  // Environment variables to be passed to the container.
  // Maximum limit is 100.
  repeated EnvVar env = 4;
}

// The spec of a Python packaged code.
message PythonPackageSpec {
  // Required. The URI of a container image in Artifact Registry that will run
  // the provided Python package. Vertex AI provides a wide range of executor
  // images with pre-installed packages to meet users' various use cases. See
  // the list of [pre-built containers for
  // training](https://cloud.google.com/vertex-ai/docs/training/pre-built-containers).
  // You must use an image from this list.
  string executor_image_uri = 1 [(google.api.field_behavior) = REQUIRED];

  // Required. The Google Cloud Storage location of the Python package files
  // which are the training program and its dependent packages. The maximum
  // number of package URIs is 100.
  repeated string package_uris = 2 [(google.api.field_behavior) = REQUIRED];

  // Required. The Python module name to run after installing the packages.
  string python_module = 3 [(google.api.field_behavior) = REQUIRED];

  // Command line arguments to be passed to the Python task.
  repeated string args = 4;

  // Environment variables to be passed to the python module.
  // Maximum limit is 100.
  repeated EnvVar env = 5;
}

// All parameters related to queuing and scheduling of custom jobs.
message Scheduling {
  // Optional. This determines which type of scheduling strategy to use. Right
  // now users have two options such as STANDARD which will use regular on
  // demand resources to schedule the job, the other is SPOT which would
  // leverage spot resources alongwith regular resources to schedule
  // the job.
  enum Strategy {
    // Strategy will default to STANDARD.
    STRATEGY_UNSPECIFIED = 0;

    // Deprecated. Regular on-demand provisioning strategy.
    ON_DEMAND = 1 [deprecated = true];

    // Deprecated. Low cost by making potential use of spot resources.
    LOW_COST = 2 [deprecated = true];

    // Standard provisioning strategy uses regular on-demand resources.
    STANDARD = 3;

    // Spot provisioning strategy uses spot resources.
    SPOT = 4;

    // Flex Start strategy uses DWS to queue for resources.
    FLEX_START = 6;
  }

  // The maximum job running time. The default is 7 days.
  google.protobuf.Duration timeout = 1;

  // Restarts the entire CustomJob if a worker gets restarted.
  // This feature can be used by distributed training jobs that are not
  // resilient to workers leaving and joining a job.
  bool restart_job_on_worker_restart = 3;

  // Optional. This determines which type of scheduling strategy to use.
  Strategy strategy = 4 [(google.api.field_behavior) = OPTIONAL];

  // Optional. Indicates if the job should retry for internal errors after the
  // job starts running. If true, overrides
  // `Scheduling.restart_job_on_worker_restart` to false.
  bool disable_retries = 5 [(google.api.field_behavior) = OPTIONAL];

  // Optional. This is the maximum duration that a job will wait for the
  // requested resources to be provisioned if the scheduling strategy is set to
  // [Strategy.DWS_FLEX_START].
  // If set to 0, the job will wait indefinitely. The default is 24 hours.
  google.protobuf.Duration max_wait_duration = 6
      [(google.api.field_behavior) = OPTIONAL];
}
