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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::MachineSpec.
Specification of a single machine.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#accelerator_count
def accelerator_count() -> ::Integer- (::Integer) — The number of accelerators to attach to the machine.
#accelerator_count=
def accelerator_count=(value) -> ::Integer- value (::Integer) — The number of accelerators to attach to the machine.
- (::Integer) — The number of accelerators to attach to the machine.
#accelerator_type
def accelerator_type() -> ::Google::Cloud::AIPlatform::V1::AcceleratorType- (::Google::Cloud::AIPlatform::V1::AcceleratorType) — Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
#accelerator_type=
def accelerator_type=(value) -> ::Google::Cloud::AIPlatform::V1::AcceleratorType- value (::Google::Cloud::AIPlatform::V1::AcceleratorType) — Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
- (::Google::Cloud::AIPlatform::V1::AcceleratorType) — Immutable. The type of accelerator(s) that may be attached to the machine as per accelerator_count.
#machine_type
def machine_type() -> ::String-
(::String) — Immutable. The type of the machine.
See the list of machine types supported for prediction
See the list of machine types supported for custom training.
For DeployedModel this field is optional, and the default value is
n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
#machine_type=
def machine_type=(value) -> ::String-
value (::String) — Immutable. The type of the machine.
See the list of machine types supported for prediction
See the list of machine types supported for custom training.
For DeployedModel this field is optional, and the default value is
n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
-
(::String) — Immutable. The type of the machine.
See the list of machine types supported for prediction
See the list of machine types supported for custom training.
For DeployedModel this field is optional, and the default value is
n1-standard-2. For BatchPredictionJob or as part of WorkerPoolSpec this field is required.
#reservation_affinity
def reservation_affinity() -> ::Google::Cloud::AIPlatform::V1::ReservationAffinity- (::Google::Cloud::AIPlatform::V1::ReservationAffinity) — Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
#reservation_affinity=
def reservation_affinity=(value) -> ::Google::Cloud::AIPlatform::V1::ReservationAffinity- value (::Google::Cloud::AIPlatform::V1::ReservationAffinity) — Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
- (::Google::Cloud::AIPlatform::V1::ReservationAffinity) — Optional. Immutable. Configuration controlling how this resource pool consumes reservation.
#tpu_topology
def tpu_topology() -> ::String- (::String) — Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
#tpu_topology=
def tpu_topology=(value) -> ::String- value (::String) — Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").
- (::String) — Immutable. The topology of the TPUs. Corresponds to the TPU topologies available from GKE. (Example: tpu_topology: "2x2x1").