Enum gapi_grpc::google::cloud::datalabeling::v1beta1::evaluation_job::State [−][src]
State of the job.
Variants
The job is scheduled to run at the [configured interval][google.cloud.datalabeling.v1beta1.EvaluationJob.schedule]. You can [pause][google.cloud.datalabeling.v1beta1.DataLabelingService.PauseEvaluationJob] or [delete][google.cloud.datalabeling.v1beta1.DataLabelingService.DeleteEvaluationJob] the job.
When the job is in this state, it samples prediction input and output from your model version into your BigQuery table as predictions occur.
The job is currently running. When the job runs, Data Labeling Service does several things:
-
If you have configured your job to use Data Labeling Service for ground truth labeling, the service creates a [Dataset][google.cloud.datalabeling.v1beta1.Dataset] and a labeling task for all data sampled since the last time the job ran. Human labelers provide ground truth labels for your data. Human labeling may take hours, or even days, depending on how much data has been sampled. The job remains in the
RUNNING
state during this time, and it can even be running multiple times in parallel if it gets triggered again (for example 24 hours later) before the earlier run has completed. When human labelers have finished labeling the data, the next step occurs.
If you have configured your job to provide your own ground truth labels, Data Labeling Service still creates a [Dataset][google.cloud.datalabeling.v1beta1.Dataset] for newly sampled data, but it expects that you have already added ground truth labels to the BigQuery table by this time. The next step occurs immediately. -
Data Labeling Service creates an [Evaluation][google.cloud.datalabeling.v1beta1.Evaluation] by comparing your model version’s predictions with the ground truth labels.
If the job remains in this state for a long time, it continues to sample prediction data into your BigQuery table and will run again at the next interval, even if it causes the job to run multiple times in parallel.
The job is not sampling prediction input and output into your BigQuery table and it will not run according to its schedule. You can [resume][google.cloud.datalabeling.v1beta1.DataLabelingService.ResumeEvaluationJob] the job.
The job has this state right before it is deleted.
Implementations
impl State
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pub fn is_valid(value: i32) -> bool
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Returns true
if value
is a variant of State
.
pub fn from_i32(value: i32) -> Option<State>
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Converts an i32
to a State
, or None
if value
is not a valid variant.
Trait Implementations
impl Clone for State
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impl Copy for State
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impl Debug for State
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impl Default for State
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impl Eq for State
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impl From<State> for i32
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impl Hash for State
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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,
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H: Hasher,
impl Ord for State
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fn cmp(&self, other: &State) -> Ordering
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#[must_use]pub fn max(self, other: Self) -> Self
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#[must_use]pub fn min(self, other: Self) -> Self
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#[must_use]pub fn clamp(self, min: Self, max: Self) -> Self
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impl PartialEq<State> for State
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impl PartialOrd<State> for State
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fn partial_cmp(&self, other: &State) -> Option<Ordering>
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#[must_use]pub fn lt(&self, other: &Rhs) -> bool
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#[must_use]pub fn le(&self, other: &Rhs) -> bool
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#[must_use]pub fn gt(&self, other: &Rhs) -> bool
1.0.0[src]
#[must_use]pub fn ge(&self, other: &Rhs) -> bool
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impl StructuralEq for State
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impl StructuralPartialEq for State
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Auto Trait Implementations
impl RefUnwindSafe for State
impl Send for State
impl Sync for State
impl Unpin for State
impl UnwindSafe for State
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>,