# pytorch-macOS-cuda

> pytorch 1.9.1+ enabling distributed by tensorpipe + cuda-mpi+ mpi + gloo on macOS 10.13.6 with cuda 10.1, cudnn 7.6.5, orlando's nccl 2.9.6

**Wikidata**: [Q119576453](https://www.wikidata.org/wiki/Q119576453)  
**Source**: https://4ort.xyz/entity/pytorch-macos-cuda

## Summary
pytorch-macOS-cuda refers to a version of PyTorch (1.9.1+) configured for macOS systems to support distributed computing using CUDA acceleration. It enables high-performance deep learning training through backends like TensorPipe, Gloo, MPI, and CUDA-MPI, utilizing specific dependencies such as CUDA 10.1, cuDNN 7.6.5, and Orlando's NCCL 2.9.6 on macOS 10.13.6.

## Key Facts
- Based on **PyTorch version 1.9.1** or newer.
- Supports **distributed computing** via TensorPipe, Gloo, MPI, and CUDA-MPI.
- Designed for **macOS 10.13.6**.
- Requires **CUDA 10.1**, **cuDNN 7.6.5**, and **NCCL 2.9.6** (Orlando’s build).
- Classified as **free software**.
- Built using the **Python programming language**.
- Described in academic literature including *PyTorch: An Imperative Style, High-Performance Deep Learning Library*.

## FAQs
### Q: What is pytorch-macOS-cuda used for?
A: It allows developers and researchers to run distributed deep learning workloads on macOS with NVIDIA GPU acceleration. It supports multiple communication backends for efficient multi-GPU or multi-node training.

### Q: Which versions of macOS and CUDA does it support?
A: This configuration targets macOS 10.13.6 specifically and uses CUDA 10.1 along with cuDNN 7.6.5 and NCCL 2.9.6.

### Q: Is pytorch-macOS-cuda open-source?
A: Yes, it falls under the category of free software, meaning users can freely use, modify, and redistribute it according to applicable licenses.

## Why It Matters
pytorch-macOS-cuda represents a specialized configuration of PyTorch tailored for users who need to leverage NVIDIA GPUs for machine learning tasks within Apple's older macOS environments. While macOS has limited native support for CUDA beyond certain legacy versions, this setup bridges that gap by enabling access to key performance libraries—such as NCCL and cuDNN—that are essential for scalable deep learning training. By integrating multiple distributed computing backends like Gloo, MPI, and TensorPipe, it offers flexibility in deployment across research labs and development teams working in heterogeneous hardware environments. Its availability helps extend the usability of PyTorch into niche but still relevant platforms where upgrading OS infrastructure may not be feasible.

## Notable For
- Enabling **CUDA-based acceleration** on **legacy macOS versions** (10.13.6), which normally lack official CUDA driver support.
- Integrating **multiple distributed training backends**: TensorPipe, Gloo, MPI, and CUDA-MPI.
- Using **Orlando's custom NCCL 2.9.6 build**, optimized for non-standard platform compatibility.
- Supporting **PyTorch 1.9.1+**, ensuring access to modern features while maintaining backward compatibility.
- Being part of the broader **free software ecosystem**, aligning with open science and reproducibility goals.

## Body
### Overview
pytorch-macOS-cuda is a variant of the popular deep learning framework PyTorch designed for execution on macOS systems equipped with NVIDIA GPUs. It provides support for distributed training capabilities using several backend technologies including TensorPipe, Gloo, MPI, and CUDA-MPI.

### Technical Configuration
This distribution requires precise alignment between system components:
- Operating System: **macOS 10.13.6**
- Compute Framework: **CUDA 10.1**
- Neural Network Primitives: **cuDNN 7.6.5**
- Communication Library: **NCCL 2.9.6** (customized by Orlando)

These constraints reflect both dependency requirements and limitations imposed by deprecated drivers and kernel extensions on macOS.

### Distributed Training Backends
The package supports various communication protocols critical for scaling model training:
- **TensorPipe**: A flexible point-to-point communication library developed for PyTorch.
- **Gloo**: Facebook's collective communications library, widely adopted in distributed ML settings.
- **MPI**: Message Passing Interface, commonly used in HPC contexts.
- **CUDA-MPI**: Extension allowing direct GPU memory transfers over MPI channels.

Each backend serves different deployment needs, from single-machine multi-GPU setups to large-scale cluster training.

### Licensing and Accessibility
As an instance of **free software**, pytorch-macOS-cuda adheres to licensing terms that permit inspection, modification, redistribution, and commercial usage without restriction. The underlying codebase benefits from contributions made possible under these freedoms.

### Academic Recognition
It is referenced in scholarly works discussing PyTorch architecture and optimization strategies, particularly those emphasizing imperative-style interfaces and cross-platform scalability (*e.g.*, *PyTorch: An Imperative Style, High-Performance Deep Learning Library*).