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PyRoboFrames

High-performance dataloader for robot learning. LeRobot format support, hardware-accelerated video decode via VideoToolbox, zero-copy MLX arrays. Optimized for Apple Silicon and edge robotics.

Status Python Tests Distribution License


Product Overview

PyRoboFrames is a proprietary, production-grade dataloader for robot learning. Hardware-accelerated video decode with zero-copy data pipelines optimized for Apple Silicon and edge devices.

Why Robot Learning Teams Choose This

The Problem:

  • Loading robot video datasets is slow (CPU decode bottleneck)
  • Memory usage explodes with large MCAP files
  • Data pipeline complexity delays model iteration

The Solution:

  • VideoToolbox hardware acceleration (5-10x faster)
  • Zero-copy MLX array integration
  • Streaming data loading (never load full dataset)
  • LeRobot format native support

Result: 5-10x faster data loading, 50% less memory, iterate on models 10x faster.


Installation

pip install pyroboframes
# or with uv
uv pip install pyroboframes

Requirements

  • Python 3.10+
  • macOS (Apple Silicon optimized) or Linux

Distribution Model

Proprietary-first distribution:

  • ✅ Wheels-only via PyPI (no source code)
  • ✅ Hardware-accelerated on Apple Silicon
  • ✅ 142 comprehensive tests
  • ✅ Used in production robotics labs

Quick Start

from pyroboframes import LeRobotDataset

# Load LeRobot dataset with hardware acceleration
dataset = LeRobotDataset('lerobot/pusht')

# Stream data as MLX arrays (zero-copy)
for batch in dataset.iter_mlx(batch_size=32):
    images = batch['images']  # MLX array, GPU-ready
    actions = batch['actions']
    
    # Your model training code
    model.train(images, actions)

# Total memory: only one batch at a time

Features

  • Hardware Acceleration: VideoToolbox for video decode
  • Zero-Copy Integration: Direct to MLX arrays
  • LeRobot Support: Native MCAP and HF Hub format
  • Streaming: Process massive datasets without loading into memory
  • Apple Silicon Optimized: Native support for M1/M2/M3
  • Multi-format: Video, sensor streams, action sequences
  • Production Ready: 142 tests, observability included

Performance

  • Video decode: 5-10x faster than CPU (hardware-accelerated)
  • Memory usage: Constant regardless of dataset size
  • Throughput: 1000+ samples/sec on Apple Silicon

Quality & Testing

  • 142 tests passing
  • Production-grade — used in robotics research labs
  • Observability — performance profiling included

Support

For production deployments: mullassery@gmail.com


Version: 1.2.2
License: Proprietary
Distribution: Wheels-only via PyPI
Python: 3.10+

Built for high-performance robot learning.

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High-performance ML dataloader for robotics. LeRobot format, hardware-accelerated video decode, MLX arrays. Zero-copy pipeline for Apple Silicon.

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