# frozen_string_literal: true
# Copyright 2022 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
#
# https://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.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module AIPlatform
module V1
# 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).
# @!attribute [r] name
# @return [::String]
# Output only. Resource name of a CustomJob.
# @!attribute [rw] display_name
# @return [::String]
# Required. The display name of the CustomJob.
# The name can be up to 128 characters long and can be consist of any UTF-8
# characters.
# @!attribute [rw] job_spec
# @return [::Google::Cloud::AIPlatform::V1::CustomJobSpec]
# Required. Job spec.
# @!attribute [r] state
# @return [::Google::Cloud::AIPlatform::V1::JobState]
# Output only. The detailed state of the job.
# @!attribute [r] create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the CustomJob was created.
# @!attribute [r] start_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the CustomJob for the first time entered the
# `JOB_STATE_RUNNING` state.
# @!attribute [r] end_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the CustomJob entered any of the following states:
# `JOB_STATE_SUCCEEDED`, `JOB_STATE_FAILED`, `JOB_STATE_CANCELLED`.
# @!attribute [r] update_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time when the CustomJob was most recently updated.
# @!attribute [r] error
# @return [::Google::Rpc::Status]
# Output only. Only populated when job's state is `JOB_STATE_FAILED` or
# `JOB_STATE_CANCELLED`.
# @!attribute [rw] labels
# @return [::Google::Protobuf::Map{::String => ::String}]
# 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.
# @!attribute [rw] encryption_spec
# @return [::Google::Cloud::AIPlatform::V1::EncryptionSpec]
# 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.
# @!attribute [r] web_access_uris
# @return [::Google::Protobuf::Map{::String => ::String}]
# 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
# {::Google::Cloud::AIPlatform::V1::CustomJobSpec#enable_web_access job_spec.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.
class CustomJob
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class LabelsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class WebAccessUrisEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
# Represents the spec of a CustomJob.
# Next Id: 14
# @!attribute [rw] worker_pool_specs
# @return [::Array<::Google::Cloud::AIPlatform::V1::WorkerPoolSpec>]
# 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.
# @!attribute [rw] scheduling
# @return [::Google::Cloud::AIPlatform::V1::Scheduling]
# Scheduling options for a CustomJob.
# @!attribute [rw] service_account
# @return [::String]
# 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.
# @!attribute [rw] network
# @return [::String]
# 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.
# @!attribute [rw] base_output_directory
# @return [::Google::Cloud::AIPlatform::V1::GcsDestination]
# 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
# {::Google::Cloud::AIPlatform::V1::Trial#id 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/`
# @!attribute [rw] tensorboard
# @return [::String]
# Optional. The name of a Vertex AI {::Google::Cloud::AIPlatform::V1::Tensorboard Tensorboard} resource to which this CustomJob
# will upload Tensorboard logs.
# Format:
# `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
# @!attribute [rw] enable_web_access
# @return [::Boolean]
# 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 {::Google::Cloud::AIPlatform::V1::CustomJob#web_access_uris CustomJob.web_access_uris} or {::Google::Cloud::AIPlatform::V1::Trial#web_access_uris Trial.web_access_uris} (within
# {::Google::Cloud::AIPlatform::V1::HyperparameterTuningJob#trials HyperparameterTuningJob.trials}).
class CustomJobSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Represents the spec of a worker pool in a job.
# @!attribute [rw] container_spec
# @return [::Google::Cloud::AIPlatform::V1::ContainerSpec]
# The custom container task.
# @!attribute [rw] python_package_spec
# @return [::Google::Cloud::AIPlatform::V1::PythonPackageSpec]
# The Python packaged task.
# @!attribute [rw] machine_spec
# @return [::Google::Cloud::AIPlatform::V1::MachineSpec]
# Optional. Immutable. The specification of a single machine.
# @!attribute [rw] replica_count
# @return [::Integer]
# Optional. The number of worker replicas to use for this worker pool.
# @!attribute [rw] disk_spec
# @return [::Google::Cloud::AIPlatform::V1::DiskSpec]
# Disk spec.
class WorkerPoolSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# The spec of a Container.
# @!attribute [rw] image_uri
# @return [::String]
# Required. The URI of a container image in the Container Registry that is to be run on
# each worker replica.
# @!attribute [rw] command
# @return [::Array<::String>]
# The command to be invoked when the container is started.
# It overrides the entrypoint instruction in Dockerfile when provided.
# @!attribute [rw] args
# @return [::Array<::String>]
# The arguments to be passed when starting the container.
# @!attribute [rw] env
# @return [::Array<::Google::Cloud::AIPlatform::V1::EnvVar>]
# Environment variables to be passed to the container.
# Maximum limit is 100.
class ContainerSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# The spec of a Python packaged code.
# @!attribute [rw] executor_image_uri
# @return [::String]
# 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.
# @!attribute [rw] package_uris
# @return [::Array<::String>]
# 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.
# @!attribute [rw] python_module
# @return [::String]
# Required. The Python module name to run after installing the packages.
# @!attribute [rw] args
# @return [::Array<::String>]
# Command line arguments to be passed to the Python task.
# @!attribute [rw] env
# @return [::Array<::Google::Cloud::AIPlatform::V1::EnvVar>]
# Environment variables to be passed to the python module.
# Maximum limit is 100.
class PythonPackageSpec
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# All parameters related to queuing and scheduling of custom jobs.
# @!attribute [rw] timeout
# @return [::Google::Protobuf::Duration]
# The maximum job running time. The default is 7 days.
# @!attribute [rw] restart_job_on_worker_restart
# @return [::Boolean]
# 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.
class Scheduling
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
end
end
end