Shuffle Sampler

Randomly shuffle data for training.

See Also

• Samplers Overview - All sampling strategies
• Sequential Sampler - Sequential order
• Epoch Aware Sampler - Epoch tracking
• Data Sources Guide

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datarax.samplers.shuffle_sampler

Grain-backed shuffle sampler for known-size datasets.

logger module-attribute

logger = getLogger(__name__)

ShuffleSamplerConfig dataclass

ShuffleSamplerConfig(cacheable: bool = False, batch_stats_fn: Callable | Module | None = None, precomputed_stats: dict[str, Any] | None = None, stochastic: bool = False, stream_name: str | None = None, dataset_size: int = 0, seed: int = 0)

Bases: StructuralConfig

Configuration for Grain IndexSampler shuffling.

dataset_size class-attribute instance-attribute

dataset_size: int = 0

seed class-attribute instance-attribute

seed: int = 0

cacheable class-attribute instance-attribute

cacheable: bool = False

batch_stats_fn class-attribute instance-attribute

batch_stats_fn: Callable | Module | None = None

precomputed_stats class-attribute instance-attribute

precomputed_stats: dict[str, Any] | None = None

stochastic class-attribute instance-attribute

stochastic: bool = False

stream_name class-attribute instance-attribute

stream_name: str | None = None

ShuffleSampler

ShuffleSampler(config: ShuffleSamplerConfig, *, rngs: Rngs | None = None, name: str | None = None)

Bases: SamplerModule

Checkpointable wrapper around Grain IndexSampler.

config instance-attribute

config: ShuffleSamplerConfig

dataset_size instance-attribute

dataset_size = dataset_size

seed instance-attribute

seed = seed

position instance-attribute

position = Variable(0)

rngs instance-attribute

rngs = rngs

name instance-attribute

name = static(name)

stochastic instance-attribute

stochastic = stochastic

stream_name instance-attribute

stream_name = stream_name

create_static_iterator staticmethod

create_static_iterator(dataset_size: int, seed: int = 0) -> Iterator[int]

Create a one-epoch Grain IndexSampler iterator.

get_state

get_state() -> dict[str, Any]

Return checkpoint state for replaying the current Grain order.

set_state

set_state(state: dict[str, Any]) -> None

Restore checkpoint state for deterministic replay.

reset

reset(seed: int | None = None) -> None

Reset the sampler to the beginning of an epoch.

get_operation_stats

get_operation_stats() -> dict[str, int]

Get operation statistics.

Note: This method converts JAX arrays to Python ints for introspection. It is intended for use outside of JIT-compiled functions.

Returns:

Type	Description
dict[str, int]	Dictionary with 'applied_count' and 'skipped_count'

reset_operation_stats

reset_operation_stats() -> None

Reset operation statistics to zero.

Note: Creates new JAX arrays to reset the counters.

compute_statistics

compute_statistics(data: Any) -> dict[str, Any] | None

Compute statistics from data using batch_stats_fn.

If batch_stats_fn is not configured, returns None. Computed statistics are cached in _computed_stats.

Parameters:

Name	Type	Description	Default
data	Any	Input data to compute statistics from	required

Returns:

Type	Description
dict[str, Any] | None	Dictionary of statistics, or None if no batch_stats_fn configured

get_statistics

get_statistics() -> dict[str, Any] | None

Get current statistics.

Returns precomputed_stats if configured (unless reset was called), otherwise returns cached computed statistics, or None if no statistics available.

Returns:

Type	Description
dict[str, Any] | None	Dictionary of statistics, or None if no statistics available

set_statistics

set_statistics(stats: dict[str, Any]) -> None

Manually set statistics.

This overwrites any previously computed statistics and clears reset flag.

Parameters:

Name	Type	Description	Default
stats	dict[str, Any]	Dictionary of statistics to set	required

reset_statistics

reset_statistics() -> None

Reset all statistics to None.

This clears both computed statistics and marks that precomputed_stats should be ignored (via internal flag). After reset, get_statistics() will return None until new statistics are set or computed.

reset_cache

reset_cache() -> None

Clear the cache.

Only has effect if cacheable=True in config.

copy

copy(*, config: DataraxModuleConfig | None = None, rngs: Rngs | None = None, name: str | None = None) -> DataraxModule

Create a copy of this module with optional config/parameter changes.

This allows creating a new module instance with modified configuration while preserving other attributes. Useful for hyperparameter tuning.

Parameters:

Name	Type	Description	Default
config	DataraxModuleConfig | None	New config (if None, uses current config)	None
rngs	Rngs | None	New RNG state (if None, uses current rngs)	None
name	str | None	New name (if None, uses current name)	None

Returns:

Type	Description
DataraxModule	New module instance with updated parameters

Examples:

Change configuration

new_config = DataraxModuleConfig(cacheable=True) new_module = module.copy(config=new_config)

Change name only

renamed = module.copy(name="new_name")

Note

Subclasses can override this method to provide more fine-grained control over copying, such as allowing individual config field updates without requiring dataclass replace().

clone

clone() -> DataraxModule

Create a new instance with the same state as this module.

Uses NNX's clone function for proper deep cloning of all state.

Returns:

Type	Description
DataraxModule	A new module instance with the same state.

requires_rng_streams

requires_rng_streams() -> list[str] | None

Get the list of RNG streams required by this module.

Returns:

Type	Description
list[str] | None	A list of required RNG stream names, or None if no RNG is required.

ensure_rng_streams

ensure_rng_streams(stream_names: list[str]) -> None

Ensure that the required RNG streams are available.

Parameters:

Name	Type	Description	Default
stream_names	list[str]	A list of available RNG stream names.	required

Raises:

Type	Description
ValueError	If a required RNG stream is not available.

process

process(input: Any, *args: Any, **kwargs: Any) -> Any

Process input structure.

This method transforms the structure/organization of input data without modifying the data values themselves.

Subclasses MUST implement this method.

The input/output types depend on the specific structural processor:

• Batcher: list[Element] -> list[Batch]
• Sampler: int -> list[int]
• Sharder: Batch -> Sharded[Batch]
• Splitter: Dataset -> tuple[Dataset, Dataset]

Parameters:

Name	Type	Description	Default
input	Any	Input to process (type varies by processor)	required
*args	Any	Additional positional arguments (processor-specific)	()
**kwargs	Any	Additional keyword arguments (processor-specific)	{}

Returns:

Type	Description
Any	Processed output (type varies by processor)

Examples:

Batcher implementation:

def process(self, elements: list[Element]) -> list[Batch]:
    batches = []
    for i in range(0, len(elements), self.config.batch_size):
        batch_elements = elements[i:i + self.config.batch_size]
        batches.append(Batch.from_elements(batch_elements))
    return batches

Sampler implementation (deterministic):

def process(self, dataset_size: int) -> list[int]:
    return list(range(min(self.config.num_samples, dataset_size)))

Sampler implementation (stochastic):

def process(self, dataset_size: int) -> list[int]:
    rng = self.rngs[self.config.stream_name]()
    indices = jax.random.choice(
        rng, dataset_size, shape=(self.config.num_samples,),
        replace=self.config.replacement
    )
    return indices.tolist()

sample

sample(n: int) -> list[int]

Return a list of sampled indices.

This method returns all indices that would be yielded by the iterator, collected into a list. This is useful when you need all indices upfront rather than iterating through them one by one.

Parameters:

Name	Type	Description	Default
n	int	The number of indices to sample (typically the dataset size).	required

Returns:

Type	Description
list[int]	A list of sampled indices.

Note

The default implementation simply collects all indices from the iterator. Subclasses may override this for more efficient implementations.

index_spec

index_spec() -> Any

Return a jax.ShapeDtypeStruct (or PyTree thereof) describing emitted indices.

The default implementation returns a scalar int32 spec, matching the common case of one-index-per-call samplers (sequential, shuffle, range). Specialized samplers (SlidingWindowSampler, BufferSampler) override this to declare windowed or vectorized index shapes.

Returns:

Type	Description
Any	A jax.ShapeDtypeStruct (or PyTree thereof) describing the
Any	shape and dtype of one emitted index.