#distributed-training

#distributed-training

Discord has detailed how it rebuilt its machine learning platform after hitting the limits of single-GPU training. By standardising on Ray and Kubernetes, introducing a one-command cluster CLI, and automating workflows through Dagster and KubeRay, the company turned distributed training into a routine operation. The changes enabled daily retrains for large models and contributed to a 200% uplift in a key ads ranking metric. Similar engineering reports are emerging from companies such as Uber, Pinterest, and Spotify as bespoke models grow in size and frequency.

Meta's PyTorch team has unveiled Monarch, an open-source framework designed to simplify distributed AI workflows across multiple GPUs and machines. The system introduces a single-controller model that allows one script to coordinate computation across an entire cluster, reducing the complexity of large-scale training and reinforcement learning tasks without changing how developers write standard PyTorch code. Monarch replaces the traditional multi-controller approach, in which multiple copies of the same script run independently across machines, with a single-controller model.