Core Concepts

This guide explains the Three-Tier Architecture of Datarax, designed to provide a clear separation of concerns between state management, parametric operations, and structural data organization.

Architecture Guidelines

The Datarax architecture is built on three hierarchical tiers:

1. Tier 1: DataraxModule (Base Foundation)
2. Tier 2A: OperatorModule (Parametric & Learnable)
3. Tier 2B: StructuralModule (Non-Parametric & Structural)

This conceptual separation ensures that components are highly composable, type-safe, and easy to reason about.

graph TD
    A[DataraxModule] --> B[OperatorModule]
    A[DataraxModule] --> C[StructuralModule]
    B --> D[Operators]
    C --> E[Data Sources]
    C --> F[Batchers]
    C --> G[Samplers]
    C --> H[Sharders]

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Tier 1: DataraxModule (The Foundation)

DataraxModule is the base class for ALL components in the library. It inherits from flax.nnx.Module, providing the fundamental capabilities required for robust state management in JAX.

Key Capabilities

• State Management: Automatically tracks state (parameters, RNG keys, metrics) using Flax NNX.
• Checkpointing: Integration with Orbax for saving and restoring full pipeline state.
• Statistics: Built-in support for computing and tracking batch statistics.
• Caching: Mechanisms to cache processed results for efficiency.
• Iteration Tracking: Keeps track of the number of iterations/calls.

All Datarax components, regardless of their specific role, share this common DNA.

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Tier 2A: OperatorModule (Parametric Transformations)

OperatorModule represents the "compute" layer of your data pipeline. These modules perform differentiable, parametric transformations on data.

Characteristics

• Parametric: Can have learnable parameters (e.g., weights in a normalization layer).
• Differentiable: Fully compatible with JAX's automatic differentiation (jax.grad).
• Input/Output: Expects and returns a Batch of data.

• Modes:

  • Deterministic: output = f(input) (e.g., Resize, Crop, Normalize)
  • Stochastic: output = f(input, rng) (e.g., RandomFlip, ColorJitter, Mixup)

Operator Definition

An OperatorModule is a unified abstraction for all data transformations. Whether a transformation is deterministic (like resizing an image) or stochastic (like adding random noise), it is implemented as an Operator.

This abstraction simplifies the mental model: everything that changes data values is an Operator.

Usage Example

from datarax.core.operator import OperatorModule, OperatorConfig import flax.nnx as nnx import jax

Defining a custom Deterministic Operator

class NormalizeOperator(OperatorModule): def init(self, config, mean, std): super().init(config) self.mean = mean self.std = std

def apply(self, data, state, metadata, random_params=None, stats=None):
    # Simplified implementation
    return data, state, metadata

Defining a custom Stochastic Operator

class RandomFlipOperator(OperatorModule): def apply(self, data, state, metadata, random_params=None, stats=None): # random_params would contain the key here return data, state, metadata

Instantiation

norm_op = NormalizeOperator( OperatorConfig(stochastic=False), mean=0.5, std=0.5 )

augment_op = RandomFlipOperator( OperatorConfig(stochastic=True, stream_name="augment"), rngs=nnx.Rngs(augment=42) )

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Tier 2B: StructuralModule (Data Organization)

StructuralModule represents the "organization" layer. These modules change the structure or arrangement of data but do not modify the data values themselves in a learnable way.

Characteristics

• Non-Parametric: Configuration is static and known at compile-time (e.g., batch size).
• Metadata-Aware: Handles data organization, batching, and distribution.
• Immutable Config: Uses frozen configuration classes to ensure structural stability.
• Input/Output: Flexible (can be individual elements, batches, or indices).

Key Implementations

1. Data Sources: specialized StructuralModule that yields initial data Element.
2. Batchers: Group individual elements into a Batch.
3. Samplers: Generate sequences of indices for data retrieval.
4. Sharders: Split batches across multiple devices (GPUs/TPUs).

Usage Example

from datarax.core.structural import StructuralConfig, StructuralModule
from datarax.core.batcher import BatcherModule
import flax.nnx as nnx

# Defining a custom Batcher
class SimpleBatcher(BatcherModule):
    def process(self, elements, *args, batch_size, drop_remainder=False, **kwargs):
        # Simplified batching logic for demonstration
        batch = []
        for element in elements:
            batch.append(element)
            if len(batch) == batch_size:
                yield batch
                batch = []
        if not drop_remainder and batch:
            yield batch

# Instantiation (batch_size is NOT in init)
batcher = SimpleBatcher(
    StructuralConfig(stochastic=False)
)

# Usage
# batches = list(batcher(data_stream, batch_size=32))

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Summary of Differences

Feature	OperatorModule	StructuralModule
Primary Role	Data Transformation	Data Organization
Learnable?	Yes	No
Differentiable?	Yes	No
Configuration	Mutable/Tenable parameters	Immutable constants
Examples	Normalization, Augmentation	Batching, Sampling, Sharding

The DAG Execution Model

Datarax pipelines are constructed as a Directed Acyclic Graph (DAG) of these modules, wrapped in Node containers.

• Stage: Any nnx.Module placed in Pipeline(stages=[...]). OperatorModule subclasses get an optimized fast path; plain nnx.Modules receive the dict batch directly.
• Source: A DataSourceModule passed to Pipeline(source=...). No wrapper class needed.
• Batching: Configured via the batch_size argument on Pipeline(...). No wrapper node.

Data flows through these nodes, with DataraxModule ensuring that state is correctly propagated and managed at every step.