"At the 2025 PyTorch Conference, the PyTorch Foundation announced several initiatives aimed at advancing open, scalable AI infrastructure. The foundation welcomed Ray, the distributed computing framework, as a hosted project and introduced PyTorch Monarch, a new framework simplifying distributed AI workload across multiple machines. The event also spotlighted new open research projects, including Stanford's Marin and AI2's Olmo-Thinking, highlighting the growing push for transparency and reproducibility in foundation model development."
"The inclusion of Ray reflects the foundation's broader strategy to build a unified open ecosystem spanning model development, serving, and distributed execution. Originally developed at UC Berkeley's RISELab, Ray provides a compact set of Python primitives that make distributed computation as intuitive as writing local code, enabling developers to scale training, tuning, and inference workloads seamlessly. The addition of Ray complements other recent projects under the foundation's umbrella, including DeepSpeed for distributed training and vLLM for high-throughput inference."
"In parallel, the Meta PyTorch team introduced PyTorch Monarch, a framework designed to abstract entire GPU clusters as a single logical device. Monarch's array-like mesh interface allows developers to express parallelism using Pythonic constructs while the system automatically manages data and computation distribution. Built on a Rust-based backend, Monarch aims to combine performance with safety and reduce the cognitive load of distributed programming."
The PyTorch Foundation onboarded Ray as a hosted project and introduced PyTorch Monarch to simplify distributed AI workloads across machines. Ray offers Python primitives that make distributed computation as intuitive as local code, enabling scalable training, tuning, and inference. Ray complements projects like DeepSpeed and vLLM to form an open-source stack covering experimentation through production deployment. Monarch abstracts entire GPU clusters as a single logical device with an array-like mesh interface and a Rust backend to balance performance, safety, and reduced cognitive load. The conference emphasized open collaboration and reproducibility in foundation model research, spotlighting Marin and Olmo-Thinking.
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