Enum gapi_grpc::google::cloud::ml::v1::training_input::ScaleTier [−][src]
A scale tier is an abstract representation of the resources Cloud ML will allocate to a training job. When selecting a scale tier for your training job, you should consider the size of your training dataset and the complexity of your model. As the tiers increase, virtual machines are added to handle your job, and the individual machines in the cluster generally have more memory and greater processing power than they do at lower tiers. The number of training units charged per hour of processing increases as tiers get more advanced. Refer to the pricing guide for more details. Note that in addition to incurring costs, your use of training resources is constrained by the quota policy.
Variants
A single worker instance. This tier is suitable for learning how to use Cloud ML, and for experimenting with new models using small datasets.
Many workers and a few parameter servers.
A large number of workers with many parameter servers.
A single worker instance with a GPU.
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 setTrainingInput.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 setTrainingInput.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.
Implementations
impl ScaleTier
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pub fn is_valid(value: i32) -> bool
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Returns true
if value
is a variant of ScaleTier
.
pub fn from_i32(value: i32) -> Option<ScaleTier>
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Converts an i32
to a ScaleTier
, or None
if value
is not a valid variant.
Trait Implementations
impl Clone for ScaleTier
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impl Copy for ScaleTier
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impl Debug for ScaleTier
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impl Default for ScaleTier
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impl Eq for ScaleTier
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impl From<ScaleTier> for i32
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impl Hash for ScaleTier
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fn hash<__H: Hasher>(&self, state: &mut __H)
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pub fn hash_slice<H>(data: &[Self], state: &mut H) where
H: Hasher,
1.3.0[src]
H: Hasher,
impl Ord for ScaleTier
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fn cmp(&self, other: &ScaleTier) -> Ordering
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#[must_use]pub fn max(self, other: Self) -> Self
1.21.0[src]
#[must_use]pub fn min(self, other: Self) -> Self
1.21.0[src]
#[must_use]pub fn clamp(self, min: Self, max: Self) -> Self
1.50.0[src]
impl PartialEq<ScaleTier> for ScaleTier
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fn eq(&self, other: &ScaleTier) -> bool
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#[must_use]pub fn ne(&self, other: &Rhs) -> bool
1.0.0[src]
impl PartialOrd<ScaleTier> for ScaleTier
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fn partial_cmp(&self, other: &ScaleTier) -> Option<Ordering>
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#[must_use]pub fn lt(&self, other: &Rhs) -> bool
1.0.0[src]
#[must_use]pub fn le(&self, other: &Rhs) -> bool
1.0.0[src]
#[must_use]pub fn gt(&self, other: &Rhs) -> bool
1.0.0[src]
#[must_use]pub fn ge(&self, other: &Rhs) -> bool
1.0.0[src]
impl StructuralEq for ScaleTier
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impl StructuralPartialEq for ScaleTier
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Auto Trait Implementations
impl RefUnwindSafe for ScaleTier
impl Send for ScaleTier
impl Sync for ScaleTier
impl Unpin for ScaleTier
impl UnwindSafe for ScaleTier
Blanket Implementations
impl<T> Any for T where
T: 'static + ?Sized,
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T: 'static + ?Sized,
impl<T> Borrow<T> for T where
T: ?Sized,
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T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
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T: ?Sized,
pub fn borrow_mut(&mut self) -> &mut T
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impl<Q, K> Equivalent<K> for Q where
K: Borrow<Q> + ?Sized,
Q: Eq + ?Sized,
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K: Borrow<Q> + ?Sized,
Q: Eq + ?Sized,
pub fn equivalent(&self, key: &K) -> bool
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impl<T> From<T> for T
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T> Instrument for T
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pub fn instrument(self, span: Span) -> Instrumented<Self>
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pub fn in_current_span(self) -> Instrumented<Self>
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impl<T, U> Into<U> for T where
U: From<T>,
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U: From<T>,
impl<T> IntoRequest<T> for T
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pub fn into_request(self) -> Request<T>
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impl<T> ToOwned for T where
T: Clone,
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T: Clone,
type Owned = T
The resulting type after obtaining ownership.
pub fn to_owned(&self) -> T
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pub fn clone_into(&self, target: &mut T)
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impl<T, U> TryFrom<U> for T where
U: Into<T>,
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U: Into<T>,
type Error = Infallible
The type returned in the event of a conversion error.
pub fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>
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impl<T, U> TryInto<U> for T where
U: TryFrom<T>,
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U: TryFrom<T>,
type Error = <U as TryFrom<T>>::Error
The type returned in the event of a conversion error.
pub fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>
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impl<V, T> VZip<V> for T where
V: MultiLane<T>,
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V: MultiLane<T>,
impl<T> WithSubscriber for T
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pub fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
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S: Into<Dispatch>,