Metadata-Version: 2.4
Name: datarax
Version: 0.1.2.post1
Summary: Datarax: A high-performance, NNX-based data pipeline framework for JAX
Project-URL: Bug Tracker, https://github.com/avitai/datarax/issues
Project-URL: Documentation, https://datarax.readthedocs.io
Project-URL: Source, https://github.com/avitai/datarax
Author: Mahdi Shafiei
License: MIT License
        
        Copyright (c) 2025 Mahdi Shafiei
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        copies of the Software, and to permit persons to whom the Software is
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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License-File: LICENSE
Keywords: data-loading,flax,jax,machine-learning,nnx,workshop
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: MacOS :: MacOS X
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Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Description-Content-Type: text/markdown

# Datarax: A Data Pipeline Framework for JAX

[![CI](https://github.com/avitai/datarax/actions/workflows/ci.yml/badge.svg)](https://github.com/avitai/datarax/actions/workflows/ci.yml)
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---

> **Early Development - API Unstable**
>
> Datarax is in early development and undergoing rapid iteration.
> Breaking changes are expected. Pin to specific commits if stability is required.
> We recommend waiting for a stable release (v1.0) before using Datarax in production.

---

**Datarax** (*Data + Array/JAX*) is an extensible data pipeline framework built for JAX-based machine learning workflows. It leverages JAX's JIT compilation, automatic differentiation, and hardware acceleration to build data loading, preprocessing, and augmentation pipelines that run on CPUs, GPUs, and TPUs.

## Key Features

- **JAX-Native Design:** All core components built on JAX's functional paradigm with Flax NNX module system for state management
- **High Performance:** JIT-compiled pipelines via XLA, with built-in profiling and roofline analysis
- **DAG Execution Engine:** Graph-based pipeline construction with branching, parallel execution, caching, and rebatching nodes
- **Scalability:** Multi-device and multi-host data distribution with device mesh sharding
- **Determinism:** Reproducible pipelines by default using Grain's Feistel cipher shuffling (O(1) memory)
- **Extensibility:** Custom data sources, operators, and augmentation strategies via composable NNX modules
- **Benchmarking Suite:** Comparative benchmarks against 12+ frameworks with Calibrax-powered analysis and regression checks
- **Ecosystem Integration:** Works with Flax, Optax, Orbax, HuggingFace Datasets, and TensorFlow Datasets

## Why Datarax?

JAX has mature libraries for models (Flax), optimizers (Optax), and checkpointing (Orbax), but lacks a dedicated data pipeline framework that operates at the same level of abstraction. Existing options are either framework-agnostic loaders that return NumPy arrays (losing JIT/autodiff benefits) or wrappers around tf.data/PyTorch that introduce cross-framework overhead. Datarax aims to fill this gap. The framework is under active development with ongoing performance optimization — the architecture is functional, but throughput and API surface are still being refined.

### JAX-Native from the Ground Up

Every component — sources, operators, batchers, samplers, sharders — is a Flax NNX module. Pipeline state is managed through NNX's variable system, which means operators can hold learnable parameters, be serialized with Orbax, and participate in JAX transformations (`jit`, `vmap`, `grad`) without special handling.

### Differentiable Data Pipelines

Because operators are NNX modules, gradients flow through the entire pipeline. This enables approaches that are not possible with standard data loaders:

- [Gradient-based augmentation search](examples/advanced/differentiable/01_dada_learned_augmentation_guide.py) — replacing RL-based methods like AutoAugment with direct optimization
- [Task-optimized preprocessing](examples/advanced/differentiable/02_learned_isp_guide.py) — backpropagating task loss through every processing stage
- [Differentiable audio synthesis](examples/advanced/differentiable/03_ddsp_audio_synthesis_guide.py) — extending the same pattern to non-vision domains

See the [differentiable pipeline examples](docs/examples/advanced/differentiable/) for details.

### DAG Execution Model

Pipelines are directed acyclic graphs, not linear chains. The `>>` operator composes sequential steps, `|` creates parallel branches, and control-flow nodes (`Branch`, `Merge`, `SplitField`) handle conditional and multi-path logic. The DAG executor manages scheduling, caching, and rebatching across the graph.

### Deterministic Reproducibility

Shuffling uses Grain's Feistel cipher permutation, which generates a full-epoch permutation in O(1) memory without materializing the index array. Combined with explicit RNG key threading through every stochastic operator, pipelines produce identical output given the same seed — across restarts, devices, and host counts.

### Built-in Competitive Benchmarking

The benchmarking suite profiles datarax against 12+ frameworks (Grain, tf.data, PyTorch DataLoader, DALI, Ray Data, and others) across standardized scenarios. Results are converted to CalibraX runs for direction-aware metrics, regression gating, and W&B export. This benchmark-driven loop is how datarax tracks progress toward competitive throughput — current results and optimization status are tracked in the [benchmarking documentation](docs/benchmarks/index.md).

## Installation

```bash
# Basic installation
uv pip install datarax

# With data loading support (HuggingFace, TFDS, audio/image libs)
uv pip install "datarax[data]"

# With GPU support (CUDA 12)
uv pip install "datarax[gpu]"

# Full development installation
uv pip install "datarax[all]"
```

### macOS / Apple Silicon

```bash
# macOS CPU mode (recommended)
uv pip install "datarax[all-cpu]"
JAX_PLATFORMS=cpu python your_script.py

# Metal GPU acceleration (experimental, M1/M2/M3+)
uv pip install jax-metal
JAX_PLATFORMS=metal python your_script.py
```

> **Note:** Metal GPU acceleration is community-tested. CI runs on macOS with CPU only.

## Quick Start

```python
import jax
import jax.numpy as jnp
import numpy as np
from flax import nnx

from datarax import build_source_pipeline
from datarax.dag.nodes import OperatorNode
from datarax.operators import ElementOperator, ElementOperatorConfig
from datarax.sources import MemorySource, MemorySourceConfig
from datarax.typing import Element


def normalize(element: Element, key: jax.Array | None = None) -> Element:
    return element.update_data({"image": element.data["image"] / 255.0})


def augment(element: Element, key: jax.Array) -> Element:
    key1, _ = jax.random.split(key)
    flip = jax.random.bernoulli(key1, 0.5)
    new_image = jax.lax.cond(
        flip, lambda img: jnp.flip(img, axis=1), lambda img: img,
        element.data["image"],
    )
    return element.update_data({"image": new_image})


# Create in-memory data source
data = {
    "image": np.random.randint(0, 255, (1000, 28, 28, 1)).astype(np.float32),
    "label": np.random.randint(0, 10, (1000,)).astype(np.int32),
}
source = MemorySource(MemorySourceConfig(), data=data, rngs=nnx.Rngs(0))

# Build pipeline with DAG-based API
normalizer = ElementOperator(
    ElementOperatorConfig(stochastic=False), fn=normalize, rngs=nnx.Rngs(0),
)
augmenter = ElementOperator(
    ElementOperatorConfig(stochastic=True, stream_name="augmentations"),
    fn=augment, rngs=nnx.Rngs(42),
)

pipeline = (
    build_source_pipeline(source, batch_size=32)
    >> OperatorNode(normalizer)
    >> OperatorNode(augmenter)
)

# Process batches
for i, batch in enumerate(pipeline):
    if i >= 3:
        break
    print(f"Batch {i}: images {batch['image'].shape}, labels {batch['label'].shape}")
```

### Advanced: Branching and Parallel DAGs

```python
from datarax.dag.nodes import OperatorNode, Merge, Branch

# Define additional operators
def invert(element: Element, key=None) -> Element:
    return element.update_data({"image": 1.0 - element.data["image"]})

inverter = ElementOperator(
    ElementOperatorConfig(stochastic=False), fn=invert, rngs=nnx.Rngs(0),
)

def is_high_contrast(element):
    return jnp.var(element.data["image"]) > 0.1

# Build a complex DAG:
# 1. Source -> Batching
# 2. Parallel: normalizer AND inverter (| creates a Parallel node)
# 3. Merge: average the two branches
# 4. Branch: conditional path based on image variance
complex_pipeline = (
    build_source_pipeline(source, batch_size=32)
    >> (OperatorNode(normalizer) | OperatorNode(inverter))
    >> Merge("mean")
    >> Branch(
           condition=is_high_contrast,
           true_path=OperatorNode(augmenter),
           false_path=OperatorNode(normalizer),
       )
)
```

## Architecture

```text
src/datarax/
  core/         # Base modules: DataSourceModule, OperatorModule, Element, Batcher, Sampler, Sharder
  dag/          # DAG executor and node system (source, operator, batch, cache, control flow)
  sources/      # MemorySource, TFDS (eager/streaming), HuggingFace (eager/streaming), ArrayRecord, MixedSource
  operators/    # ElementOperator, MapOperator, CompositeOperator, modality-specific (image, text)
    strategies/ # Sequential, Parallel, Branching, Ensemble, Merging execution strategies
  samplers/     # Sequential, Shuffle (Feistel cipher), Range, EpochAware samplers
  sharding/     # ArraySharder, JaxProcessSharder for multi-device distribution
  distributed/  # DeviceMesh, DataParallel for multi-host training
  batching/     # DefaultBatcher with buffer state management
  checkpoint/   # NNXCheckpointHandler with Orbax integration
  monitoring/   # Pipeline monitor, DAG monitor, reporters
  performance/  # Roofline analysis, XLA optimization utilities
  control/      # Prefetcher for asynchronous data loading
  memory/       # Shared memory manager for multi-process data sharing
  config/       # TOML-based configuration system with schema validation
  cli/          # datarax CLI entry point
  utils/        # PyTree utilities, external integration helpers
```

## Benchmarking

Datarax includes a benchmarking suite for comparison against 12+ data loading frameworks across a range of workload scenarios (vision, NLP, tabular, multimodal, distributed).

```bash
# Install benchmark dependencies (adds PyTorch, DALI, Ray, etc.)
uv sync --extra benchmark

# Optional: install CalibraX with W&B support explicitly
uv pip install "calibrax[wandb] @ git+https://github.com/avitai/calibrax.git"

# Run benchmarks locally
uv run python -m benchmarks.runners.full_runner --platform cpu --repetitions 5

# Run on cloud (SkyPilot)
sky launch benchmarks/sky/gpu-benchmark.yaml --env WANDB_API_KEY=$WANDB_API_KEY
```

Benchmark results are exported to W&B with charts, gap analysis, stability reports, and raw result artifacts. See [Benchmarking Guide](docs/benchmarks/index.md) for methodology and cloud deployment.

## Development Setup

Datarax uses `uv` as its package manager:

```bash
# Clone and setup
git clone https://github.com/avitai/datarax.git
cd datarax

# Automatic setup
./setup.sh && source activate.sh

# Or manual install
uv sync --extra dev
```

### Running Tests

```bash
# CPU-only (most stable)
JAX_PLATFORMS=cpu uv run pytest

# Include benchmark test suite in the same run
JAX_PLATFORMS=cpu uv run pytest --all-suites

# Specific module
JAX_PLATFORMS=cpu uv run pytest tests/sources/test_memory_source_module.py
```

### Docker

```bash
# Build and run
docker build -t datarax:latest .
docker run --rm --gpus all datarax:latest python -c "import datarax, jax; print(jax.devices())"

# Benchmark images
docker build -f benchmarks/docker/Dockerfile.gpu -t datarax-bench:gpu .
```

See [Docker Guide](docs/contributing/docker.md) for full details.

## Documentation

- [Installation Guide](docs/getting_started/installation.md)
- [Quick Start](docs/getting_started/quick_start.md)
- [Core Concepts](docs/getting_started/core_concepts.md)
- [User Guide](docs/user_guide/)
- [API Reference](docs/api_reference/index.md)
- [Examples](docs/examples/overview.md)
- [Benchmarking](docs/benchmarks/index.md)
- [Contributing](docs/contributing/contributing_guide.md)
- [Docker](docs/contributing/docker.md)

## License

Datarax is licensed under the [MIT License](LICENSE).
