AI Platform Notebooks V1 API - Module Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier (v0.1.0)

Version 0.1.0

• 0.9.0 (latest)
• 0.8.2
• 0.7.0
• 0.6.1
• 0.5.0
• 0.4.2
• 0.3.0
• 0.2.1
• 0.1.0

Reference documentation and code samples for the AI Platform Notebooks V1 API module Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier.

Required. Specifies the machine types, the number of replicas for workers and parameter servers.

Constants

SCALE_TIER_UNSPECIFIED

value: 0
Unspecified Scale Tier.

BASIC

value: 1
A single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.

STANDARD_1

value: 2
Many workers and a few parameter servers.

PREMIUM_1

value: 3
A large number of workers with many parameter servers.

BASIC_GPU

value: 4
A single worker instance with a K80 GPU.

BASIC_TPU

value: 5
A single worker instance with a Cloud TPU.

CUSTOM

value: 6
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:

• You must set TrainingInput.masterType to specify the type of machine to use for your master node. This is the only required setting.

• You may set TrainingInput.workerCount to specify the number of workers to use. If you specify one or more workers, you must also set TrainingInput.workerType to specify the type of machine to use for your worker nodes.

• You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use. If you specify one or more parameter servers, you must also set TrainingInput.parameterServerType to specify the type of machine to use for your parameter servers.

Note that all of your workers must use the same machine type, which can be different from your parameter server type and master type. Your parameter servers must likewise use the same machine type, which can be different from your worker type and master type.