PyTorch Conference Europe 2026

PyTorch Monarch introduces a new distributed programming paradigm that enables developers to orchestrate entire GPU clusters from a single Python program. With its actor-based runtime, process mesh abstraction, and asynchronous execution model, Monarch simplifies large-scale distributed training and enables complex workflows that combine training, evaluation, and reinforcement learning within one unified script.

In this talk, we present our work enabling PyTorch Monarch on AMD Instinct GPUs with ROCm, expanding the single-controller model beyond CUDA environments and bringing this emerging runtime to a broader hardware ecosystem. We describe the engineering effort required to port Monarch’s GPU runtime and distributed communication stack to ROCm, including HIPification of CUDA-specific components, adaptation of memory management and synchronization semantics, and integration with high-performance GPU-to-GPU communication on multi-node clusters through RDMA.

We will share lessons learned from running Monarch workloads on MI300-class clusters, including performance considerations, debugging workflows, and developer experience improvements. Our results demonstrate that Monarch’s architecture can be successfully extended to heterogeneous hardware environments while preserving scalability and ease of use.

This work advances hardware diversity in distributed PyTorch and highlights how portable runtimes can simplify large-scale training while enabling scalable, cluster-wide experimentation across accelerator platforms.