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PredictivePrefetchDataset

tenflowers_dataset::predictive_prefetch

Struct PredictivePrefetchDataset 

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pub struct PredictivePrefetchDataset<T, D>
where
    T: Clone + Send + Sync + 'static,
    D: Send + Sync + 'static + Dataset<T>,
{ /* private fields */ }
Expand description

Dataset wrapper that provides predictive prefetching

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impl<T, D> PredictivePrefetchDataset<T, D>
where T: Clone + Send + Sync + 'static, D: Dataset<T> + Send + Sync + 'static,

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pub fn new(dataset: D) -> Self

Create a new predictive prefetch dataset

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pub fn with_config(dataset: D, config: PrefetchConfig) -> Self

Create with custom configuration

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pub fn stats(&self) -> AccessStats

Get access statistics

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pub fn dominant_pattern(&self) -> Option<AccessPattern>

Get the dominant access pattern

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impl<T, D> Dataset<T> for PredictivePrefetchDataset<T, D>
where T: Clone + Send + Sync + 'static, D: Dataset<T> + Send + Sync + 'static,

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fn len(&self) -> usize

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fn get(&self, index: usize) -> Result<(Tensor<T>, Tensor<T>)>

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fn is_empty(&self) -> bool

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fn batch(self, batch_size: usize) -> BatchedDataset<T, Self> ⓘ
where Self: Sized,

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impl<T, D> AdvancedStatisticsExt<T> for D
where T: Clone + Default + Float + Send + Sync + Pod + Zeroable + 'static, D: Dataset<T>,

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fn compute_multivariate_statistics( &self, ) -> Result<MultivariateStatistics<T>, TensorError>

Compute advanced multivariate statistics
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fn compute_pca(&self, n_components: usize) -> Result<PCAResult<T>, TensorError>

Perform principal component analysis
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impl<T> Any for T
where T: 'static + ?Sized,

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fn type_id(&self) -> TypeId

Gets the TypeId of self. Read more
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impl<T> Borrow<T> for T
where T: ?Sized,

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fn borrow(&self) -> &T

Immutably borrows from an owned value. Read more
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impl<T> BorrowMut<T> for T
where T: ?Sized,

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fn borrow_mut(&mut self) -> &mut T

Mutably borrows from an owned value. Read more
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impl<T, D> CacheExt<T> for D
where D: Dataset<T>,

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fn cached(self, capacity: usize) -> CachedDataset<T, Self>
where T: Clone + Send + Sync + 'static,

Wrap this dataset with caching
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fn cached_with_warming( self, capacity: usize, strategy: WarmingStrategy, ) -> Result<CachedDataset<T, Self>>
where T: Clone + Send + Sync + 'static,

Wrap this dataset with caching and pre-warm the cache
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impl<T, D> DataQualityExt<T> for D
where D: Dataset<T>,

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fn analyze_quality(&self, name: impl Into<String>) -> Result<DataQualityMetrics>
where T: Clone + Default + Zero + Float + Send + Sync + 'static,

Analyze data quality
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fn quality_report(&self, name: impl Into<String>) -> Result<String>
where T: Clone + Default + Zero + Float + Send + Sync + 'static,

Generate a quality report
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impl<T, D> DatasetExt<T> for D
where D: Dataset<T>,

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fn transform<Tr: Transform<T>>( self, transform: Tr, ) -> TransformedDataset<T, Self, Tr>

Apply a transform to this dataset
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impl<T, D> DatasetShardingExt<T> for D
where D: Dataset<T>,

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fn shard(self, config: ShardConfig) -> Result<ShardedDataset<T, Self>>

Shard this dataset for distributed training
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fn shard_round_robin( self, world_size: usize, rank: usize, ) -> Result<ShardedDataset<T, Self>>

Create a round-robin sharded dataset
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fn shard_contiguous( self, world_size: usize, rank: usize, ) -> Result<ShardedDataset<T, Self>>

Create a contiguous sharded dataset
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fn shard_shuffled( self, world_size: usize, rank: usize, seed: u64, ) -> Result<ShardedDataset<T, Self>>

Create a shuffled sharded dataset with a seed
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impl<T, D> DatasetStatisticsExt<T> for D
where T: Clone + Default + Zero + Float + Debug + Send + Sync + 'static, D: Dataset<T>,

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fn compute_statistics(&self) -> Result<DatasetStats<T>, TensorError>

Compute statistics with default configuration
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fn compute_statistics_with_config( &self, config: StatisticsConfig, ) -> Result<DatasetStats<T>, TensorError>

Compute statistics with custom configuration
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impl<T, D> DatasetUtilsExt<T> for D
where D: Dataset<T>,

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fn get_multiple(&self, indices: &[usize]) -> Result<Vec<(Tensor<T>, Tensor<T>)>>

Get multiple samples by their indices
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fn get_range( &self, start: usize, end: usize, ) -> Result<Vec<(Tensor<T>, Tensor<T>)>>

Get a range of samples from start (inclusive) to end (exclusive)
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fn get_random(&self) -> Result<(Tensor<T>, Tensor<T>)>

Get a random sample from the dataset
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fn get_random_samples( &self, count: usize, ) -> Result<Vec<(Tensor<T>, Tensor<T>)>>

Get multiple random samples from the dataset (with replacement)
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impl<T, D> DatasetValidationExt<T> for D
where D: Dataset<T>, T: Clone + Default + PartialEq + PartialOrd + Display + Float + Send + Sync + 'static,

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fn validate( &self, validator: &DataValidator<T>, ) -> Result<ValidationResult, TensorError>

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fn validate_with_config( &self, config: ValidationConfig, ) -> Result<ValidationResult, TensorError>

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fn is_valid(&self) -> Result<bool, TensorError>

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impl<T, D> DatasetVisualizationExt<T> for D
where D: Dataset<T>,

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fn sample_preview(&self, num_samples: usize) -> Result<SamplePreview>
where T: Clone + Default + Zero + Send + Sync + 'static,

Create a sample preview
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fn feature_distribution( &self, max_samples: Option<usize>, ) -> Result<DistributionInfo<T>>
where T: Clone + Default + Zero + Send + Sync + 'static + Float,

Get feature distribution information
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fn class_distribution(&self) -> Result<ClassDistribution>
where T: Clone + Default + Zero + Send + Sync + 'static,

Get class distribution
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fn feature_histogram( &self, feature_index: usize, bins: usize, ) -> Result<FeatureHistogram<T>>
where T: Clone + Default + Zero + Send + Sync + 'static + Float + PartialOrd,

Create a histogram for a specific feature
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fn analyze_augmentation_effects<Tr>( &self, transform: &Tr, num_samples: usize, ) -> Result<AugmentationEffects<T>>
where T: Clone + Default + Zero + Send + Sync + 'static + Float + PartialOrd, Tr: Transform<T>,

Analyze the effects of a transform on dataset samples
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impl<T, D> FederatedDatasetExt<T> for D
where D: Dataset<T>, T: Clone + Default + Send + Sync + 'static,

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fn federated_client( self, config: ClientConfig, ) -> FederatedClientDataset<T, Self>

Create a federated client dataset
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fn partition_federated( self, num_clients: usize, strategy: PartitioningStrategy, seed: u64, ) -> Result<Vec<FederatedClientDataset<T, ClientIndexedDataset<T, Self>>>>
where Self: Clone,

Partition dataset for federated learning
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impl<T> From<T> for T

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fn from(t: T) -> T

Returns the argument unchanged.

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impl<T> Instrument for T

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fn instrument(self, span: Span) -> Instrumented<Self>

Instruments this type with the provided Span, returning an Instrumented wrapper. Read more
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fn in_current_span(self) -> Instrumented<Self>

Instruments this type with the current Span, returning an Instrumented wrapper. Read more
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impl<T, U> Into<U> for T
where U: From<T>,

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fn into(self) -> U

Calls U::from(self).

That is, this conversion is whatever the implementation of From<T> for U chooses to do.

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impl<T> IntoEither for T

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fn into_either(self, into_left: bool) -> Either<Self, Self>

Converts self into a Left variant of Either<Self, Self> if into_left is true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
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fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
where F: FnOnce(&Self) -> bool,

Converts self into a Left variant of Either<Self, Self> if into_left(&self) returns true. Converts self into a Right variant of Either<Self, Self> otherwise. Read more
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impl<T> Pointable for T

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const ALIGN: usize

The alignment of pointer.
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type Init = T

The type for initializers.
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unsafe fn init(init: <T as Pointable>::Init) -> usize

Initializes a with the given initializer. Read more
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unsafe fn deref<'a>(ptr: usize) -> &'a T

Dereferences the given pointer. Read more
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unsafe fn deref_mut<'a>(ptr: usize) -> &'a mut T

Mutably dereferences the given pointer. Read more
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unsafe fn drop(ptr: usize)

Drops the object pointed to by the given pointer. Read more
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impl<T> PolicyExt for T
where T: ?Sized,

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fn and<P, B, E>(self, other: P) -> And<T, P>
where T: Policy<B, E>, P: Policy<B, E>,

Create a new Policy that returns Action::Follow only if self and other return Action::Follow. Read more
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fn or<P, B, E>(self, other: P) -> Or<T, P>
where T: Policy<B, E>, P: Policy<B, E>,

Create a new Policy that returns Action::Follow if either self or other returns Action::Follow. Read more
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impl<T, D> ReproducibilityExt<T> for D
where D: Dataset<T>, T: Clone + Default + Send + Sync + 'static,

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fn deterministic(self, seed: u64) -> DeterministicDataset<T, Self>

Make the dataset deterministic with a seed
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fn sequential(self) -> DeterministicDataset<T, Self>

Make the dataset sequential
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fn reverse(self) -> DeterministicDataset<T, Self>

Make the dataset reverse ordered
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
where U: TryFrom<T>,

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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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fn try_into(self) -> Result<U, <U as TryFrom<T>>::Error>

Performs the conversion.
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impl<V, T> VZip<V> for T
where V: MultiLane<T>,

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fn vzip(self) -> V

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impl<T> WithSubscriber for T

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fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self>
where S: Into<Dispatch>,

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Attaches the current default Subscriber to this type, returning a WithDispatch wrapper. Read more