trainer

Why we included this project

Running training on Kubernetes means dealing with pods, workers, and cluster scheduling before you ever get to the model. Kubeflow Trainer is the layer that takes that plumbing out of your hands: TrainJob and Runtime APIs turn multi-node, multi-GPU runs into declarative resources, so a data scientist can fire off a PyTorch or DeepSpeed job without hand-wiring anything. LLM fine-tuning at scale is where it earns its keep, since a distributed data cache streams tensors straight to GPU nodes and keeps I/O off the critical path. It also slots into the wider cloud-native stack, coordinating with Kueue for scheduling and JobSet or LeaderWorkerSet for workload management. If your platform is already Kubernetes, this gives you reproducible, cluster-aware training runs instead of yet another script glued together with kubectl.