# 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 Notebooks
module V1
# The description a notebook execution workload.
# @!attribute [rw] scale_tier
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier]
# Required. Scale tier of the hardware used for notebook execution.
# DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
# @!attribute [rw] master_type
# @return [::String]
# Specifies the type of virtual machine to use for your training
# job's master worker. You must specify this field when `scaleTier` is set to
# `CUSTOM`.
#
# You can use certain Compute Engine machine types directly in this field.
# The following types are supported:
#
# - `n1-standard-4`
# - `n1-standard-8`
# - `n1-standard-16`
# - `n1-standard-32`
# - `n1-standard-64`
# - `n1-standard-96`
# - `n1-highmem-2`
# - `n1-highmem-4`
# - `n1-highmem-8`
# - `n1-highmem-16`
# - `n1-highmem-32`
# - `n1-highmem-64`
# - `n1-highmem-96`
# - `n1-highcpu-16`
# - `n1-highcpu-32`
# - `n1-highcpu-64`
# - `n1-highcpu-96`
#
#
# Alternatively, you can use the following legacy machine types:
#
# - `standard`
# - `large_model`
# - `complex_model_s`
# - `complex_model_m`
# - `complex_model_l`
# - `standard_gpu`
# - `complex_model_m_gpu`
# - `complex_model_l_gpu`
# - `standard_p100`
# - `complex_model_m_p100`
# - `standard_v100`
# - `large_model_v100`
# - `complex_model_m_v100`
# - `complex_model_l_v100`
#
#
# Finally, if you want to use a TPU for training, specify `cloud_tpu` in this
# field. Learn more about the [special configuration options for training
# with
# TPU](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
# @!attribute [rw] accelerator_config
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig]
# Configuration (count and accelerator type) for hardware running notebook
# execution.
# @!attribute [rw] labels
# @return [::Google::Protobuf::Map{::String => ::String}]
# Labels for execution.
# If execution is scheduled, a field included will be 'nbs-scheduled'.
# Otherwise, it is an immediate execution, and an included field will be
# 'nbs-immediate'. Use fields to efficiently index between various types of
# executions.
# @!attribute [rw] input_notebook_file
# @return [::String]
# Path to the notebook file to execute.
# Must be in a Google Cloud Storage bucket.
# Format: `gs://{bucket_name}/{folder}/{notebook_file_name}`
# Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb`
# @!attribute [rw] container_image_uri
# @return [::String]
# Container Image URI to a DLVM
# Example: 'gcr.io/deeplearning-platform-release/base-cu100'
# More examples can be found at:
# https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
# @!attribute [rw] output_notebook_folder
# @return [::String]
# Path to the notebook folder to write to.
# Must be in a Google Cloud Storage bucket path.
# Format: `gs://{bucket_name}/{folder}`
# Ex: `gs://notebook_user/scheduled_notebooks`
# @!attribute [rw] params_yaml_file
# @return [::String]
# Parameters to be overridden in the notebook during execution.
# Ref https://papermill.readthedocs.io/en/latest/usage-parameterize.html on
# how to specifying parameters in the input notebook and pass them here
# in an YAML file.
# Ex: `gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml`
# @!attribute [rw] parameters
# @return [::String]
# Parameters used within the 'input_notebook_file' notebook.
# @!attribute [rw] service_account
# @return [::String]
# The email address of a service account to use when running the execution.
# You must have the `iam.serviceAccounts.actAs` permission for the specified
# service account.
# @!attribute [rw] job_type
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType]
# The type of Job to be used on this execution.
# @!attribute [rw] dataproc_parameters
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters]
# Parameters used in Dataproc JobType executions.
# @!attribute [rw] vertex_ai_parameters
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::VertexAIParameters]
# Parameters used in Vertex AI JobType executions.
# @!attribute [rw] kernel_spec
# @return [::String]
# Name of the kernel spec to use. This must be specified if the
# kernel spec name on the execution target does not match the name in the
# input notebook file.
# @!attribute [rw] tensorboard
# @return [::String]
# The name of a Vertex AI [Tensorboard] resource to which this execution
# will upload Tensorboard logs.
# Format:
# `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
class ExecutionTemplate
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Definition of a hardware accelerator. Note that not all combinations
# of `type` and `core_count` are valid. Check [GPUs on
# Compute Engine](https://cloud.google.com/compute/docs/gpus) to find a valid
# combination. TPUs are not supported.
# @!attribute [rw] type
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorType]
# Type of this accelerator.
# @!attribute [rw] core_count
# @return [::Integer]
# Count of cores of this accelerator.
class SchedulerAcceleratorConfig
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Parameters used in Dataproc JobType executions.
# @!attribute [rw] cluster
# @return [::String]
# URI for cluster used to run Dataproc execution.
# Format: `projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}`
class DataprocParameters
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Parameters used in Vertex AI JobType executions.
# @!attribute [rw] network
# @return [::String]
# The full name of the Compute Engine
# [network](https://cloud.google.com/compute/docs/networks-and-firewalls#networks)
# to which the Job should be peered. For example,
# `projects/12345/global/networks/myVPC`.
# [Format](https://cloud.google.com/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.
#
# Private services access must already be configured for the network. If
# left unspecified, the job is not peered with any network.
# @!attribute [rw] env
# @return [::Google::Protobuf::Map{::String => ::String}]
# Environment variables.
# At most 100 environment variables can be specified and unique.
# Example: `GCP_BUCKET=gs://my-bucket/samples/`
class VertexAIParameters
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class EnvEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
end
# @!attribute [rw] key
# @return [::String]
# @!attribute [rw] value
# @return [::String]
class LabelsEntry
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
end
# Required. Specifies the machine types, the number of replicas for workers
# and parameter servers.
module ScaleTier
# Unspecified Scale Tier.
SCALE_TIER_UNSPECIFIED = 0
# 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 = 1
# Many workers and a few parameter servers.
STANDARD_1 = 2
# A large number of workers with many parameter servers.
PREMIUM_1 = 3
# A single worker instance with a K80 GPU.
BASIC_GPU = 4
# A single worker instance with a Cloud TPU.
BASIC_TPU = 5
# 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 `ExecutionTemplate.masterType` to specify the type
# of machine to use for your master node. This is the only required
# setting.
CUSTOM = 6
end
# Hardware accelerator types for AI Platform Training jobs.
module SchedulerAcceleratorType
# Unspecified accelerator type. Default to no GPU.
SCHEDULER_ACCELERATOR_TYPE_UNSPECIFIED = 0
# Nvidia Tesla K80 GPU.
NVIDIA_TESLA_K80 = 1
# Nvidia Tesla P100 GPU.
NVIDIA_TESLA_P100 = 2
# Nvidia Tesla V100 GPU.
NVIDIA_TESLA_V100 = 3
# Nvidia Tesla P4 GPU.
NVIDIA_TESLA_P4 = 4
# Nvidia Tesla T4 GPU.
NVIDIA_TESLA_T4 = 5
# Nvidia Tesla A100 GPU.
NVIDIA_TESLA_A100 = 10
# TPU v2.
TPU_V2 = 6
# TPU v3.
TPU_V3 = 7
end
# The backend used for this execution.
module JobType
# No type specified.
JOB_TYPE_UNSPECIFIED = 0
# Custom Job in `aiplatform.googleapis.com`.
# Default value for an execution.
VERTEX_AI = 1
# Run execution on a cluster with Dataproc as a job.
# https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs
DATAPROC = 2
end
end
# The definition of a single executed notebook.
# @!attribute [rw] execution_template
# @return [::Google::Cloud::Notebooks::V1::ExecutionTemplate]
# execute metadata including name, hardware spec, region, labels, etc.
# @!attribute [r] name
# @return [::String]
# Output only. The resource name of the execute. Format:
# `projects/{project_id}/locations/{location}/executions/{execution_id}`
# @!attribute [r] display_name
# @return [::String]
# Output only. Name used for UI purposes.
# Name can only contain alphanumeric characters and underscores '_'.
# @!attribute [rw] description
# @return [::String]
# A brief description of this execution.
# @!attribute [r] create_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time the Execution was instantiated.
# @!attribute [r] update_time
# @return [::Google::Protobuf::Timestamp]
# Output only. Time the Execution was last updated.
# @!attribute [r] state
# @return [::Google::Cloud::Notebooks::V1::Execution::State]
# Output only. State of the underlying AI Platform job.
# @!attribute [rw] output_notebook_file
# @return [::String]
# Output notebook file generated by this execution
# @!attribute [r] job_uri
# @return [::String]
# Output only. The URI of the external job used to execute the notebook.
class Execution
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# Enum description of the state of the underlying AIP job.
module 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 execution 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
# The job has become expired (relevant to Vertex AI jobs)
# https://cloud.google.com/vertex-ai/docs/reference/rest/v1/JobState
EXPIRED = 9
# The Execution is being created.
INITIALIZING = 10
end
end
end
end
end
end