Kubeflow Trainer v2.2: JAX & XGBoost Runtimes, Flux for HPC Support, and TrainJob progress and metrics observability

Bringing JAX to Kubernetes with Trainer Bringing XGBoost to Kubernetes with Trainer Track TrainJob Progress and Expose Metrics How it works Future Plans Bringing Flux Framework for HPC and MPI Bootstrapping Resource Timeout for TrainJobs RuntimePatches API to override TrainJob defaults Breaking Changes Replace PodTemplateOverrides with RuntimePatches API Remove numProcPerNode from the Torch MLPolicy API Remove ElasticPolicy API Some TrainJob API fields are now immutable Release Notes Roadmap Moving Forward Join the Community Contribute: Connect with the Community: Learn More: Bringing JAX to Kubernetes with Trainer Bringing XGBoost to Kubernetes with Trainer Track TrainJob Progress and Expose Metrics How it works Future Plans How it works Future Plans Bringing Flux Framework for HPC and MPI Bootstrapping Resource Timeout for TrainJobs RuntimePatches API to override TrainJob defaults Breaking Changes Replace PodTemplateOverrides with RuntimePatches API Remove numProcPerNode from the Torch MLPolicy API Remove ElasticPolicy API Some TrainJob API fields are now immutable Replace PodTemplateOverrides with RuntimePatches API Remove numProcPerNode from the Torch MLPolicy API Remove ElasticPolicy API Some TrainJob API fields are now immutable Release Notes Roadmap Moving Forward Join the Community Contribute: Connect with the Community: Learn More: Contribute: Connect with the Community: Learn More: Just a little over one week ahead of KubeCon + CloudNativeCon EU 2026, the Kubeflow team is excited to ship Trainer v2.2. The v2.2 release reinforces our commitment to expanding the Kubeflow Trainer ecosystem – meeting developers where they are by adding native support for JAX, XGBoost, and Flux, while also delivering deeper observability into training jobs. Key highlights of the v2.2 release include: First-class support for Training Runtimes for JAX and XGBoost , enabling native distributed training on Kubernetes. This marks a major milestone for the Trainer project, achieving full compatibility with Training Operator v1 CRDs: PyTorchJob, MPIJob, JAXJob, and XGBoostJob – now unified under a single TrainJob abstraction. Enhanced training observability , allowing progress and metrics to be propagated directly from training scripts to the TrainJob status. Hugging Face Transformers already integrate with the KubeflowTrainerCallback to automate this capability. Flux runtime support , bringing HPC workloads to Kubernetes and improving MPI bootstrapping within TrainJob. TrainJob activeDeadlineSeconds API , enabling explicit timeout policies for training jobs. RuntimePatches API , introducing a more flexible and scalable way to customize runtime configurations from the TrainJobs. You can now install the Kubeflow Trainer control plane and its training runtimes with a single command: helm install kubeflow-trainer oci://ghcr. io/kubeflow/charts/kubeflow-trainer \ --namespace kubeflow-system \ --create-namespace \ --version 2.2.0 \ --set runtimes. defaultEnabled = true helm install kubeflow-trainer oci://ghcr.