Class: Google::Cloud::Notebooks::V1::ExecutionTemplate

Inherits:

Object

Object
Google::Cloud::Notebooks::V1::ExecutionTemplate

show all

Extended by:: Protobuf::MessageExts::ClassMethods

Includes:: Protobuf::MessageExts

Defined in:: proto_docs/google/cloud/notebooks/v1/execution.rb

Overview

The description a notebook execution workload.

Defined Under Namespace

Modules: JobType, ScaleTier, SchedulerAcceleratorType Classes: DataprocParameters, LabelsEntry, SchedulerAcceleratorConfig, VertexAIParameters

Instance Attribute Summary collapse

#accelerator_config ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig
Configuration (count and accelerator type) for hardware running notebook execution.
#container_image_uri ⇒ ::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.
#dataproc_parameters ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters
Parameters used in Dataproc JobType executions.
#input_notebook_file ⇒ ::String
Path to the notebook file to execute.
#job_type ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType
The type of Job to be used on this execution.
#kernel_spec ⇒ ::String
Name of the kernel spec to use.
#labels ⇒ ::Google::Protobuf::Map{::String => ::String}
Labels for execution.
#master_type ⇒ ::String
Specifies the type of virtual machine to use for your training job's master worker.
#output_notebook_folder ⇒ ::String
Path to the notebook folder to write to.
#parameters ⇒ ::String
Parameters used within the 'input_notebook_file' notebook.
#params_yaml_file ⇒ ::String
Parameters to be overridden in the notebook during execution.
#scale_tier ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier deprecated Deprecated.
This field is deprecated and may be removed in the next major version update.
#service_account ⇒ ::String
The email address of a service account to use when running the execution.
#tensorboard ⇒ ::String
The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs.
#vertex_ai_parameters ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::VertexAIParameters
Parameters used in Vertex AI JobType executions.

Instance Attribute Details

#accelerator_config ⇒ `::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#container_image_uri ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#dataproc_parameters ⇒ `::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#input_notebook_file ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#job_type ⇒ `::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#kernel_spec ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#labels ⇒ `::Google::Protobuf::Map{::String => ::String}`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#master_type ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#output_notebook_folder ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#parameters ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#params_yaml_file ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#scale_tier ⇒ `::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier`

Deprecated.

This field is deprecated and may be removed in the next major version update.

Returns Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#service_account ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#tensorboard ⇒ `::String`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

#vertex_ai_parameters ⇒ `::Google::Cloud::Notebooks::V1::ExecutionTemplate::VertexAIParameters`

# File 'proto_docs/google/cloud/notebooks/v1/execution.rb', line 148

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

Class: Google::Cloud::Notebooks::V1::ExecutionTemplate

Overview

Defined Under Namespace

Instance Attribute Summary collapse

Instance Attribute Details

#accelerator_config ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig

#container_image_uri ⇒ ::String

#dataproc_parameters ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters

#input_notebook_file ⇒ ::String

#job_type ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType

#kernel_spec ⇒ ::String

#labels ⇒ ::Google::Protobuf::Map{::String => ::String}

#master_type ⇒ ::String

#output_notebook_folder ⇒ ::String

#parameters ⇒ ::String

#params_yaml_file ⇒ ::String

#scale_tier ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier

#service_account ⇒ ::String

#tensorboard ⇒ ::String

#vertex_ai_parameters ⇒ ::Google::Cloud::Notebooks::V1::ExecutionTemplate::VertexAIParameters