docs: add XGBoost distributed training user guide

Add a user guide for distributed XGBoost training on Kubernetes via
Kubeflow Trainer at content/en/docs/components/trainer/user-guides/xgboost.md.

The guide provides:
- An overview of the XGBoost Collective protocol and how Kubeflow Trainer
  integrates with it (DMLC_* env vars, JobSet, built-in runtime)
- Worker count formula for CPU and GPU training
- A redirect to the comprehensive XGBoost tutorial at
  https://xgboost.readthedocs.io/en/latest/tutorials/kubernetes.html

Signed-off-by: krishna-kg732 <krishnagupta.kg2k6@gmail.com>

Author: Krishna-kg732

content/en/docs/components/trainer/user-guides/xgboost.md

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+title = "XGBoost Guide"
+description = "How to run distributed XGBoost on Kubernetes with Kubeflow Trainer"
+weight = 20
++++
+
+This guide describes how to use TrainJob to run distributed
+[XGBoost](https://xgboost.readthedocs.io/) training on Kubernetes.
+
+---
+
+## Prerequisites
+
+Before exploring this guide, make sure to follow
+[the Getting Started guide](/docs/components/trainer/getting-started/)
+to understand the basics of Kubeflow Trainer.
+
+---
+
+## XGBoost Distributed Overview
+
+XGBoost supports distributed training through the
+[Collective](https://xgboost.readthedocs.io/en/latest/tutorials/kubernetes.html)
+communication protocol (historically known as Rabit). In a distributed setting,
+multiple worker processes each operate on a shard of the data and synchronize
+histogram bin statistics via AllReduce to agree on the best tree splits.
+
+Kubeflow Trainer integrates with XGBoost by:
+
+- Deploying worker pods as a [JobSet](https://github.com/kubernetes-sigs/jobset).
+- Automatically injecting the `DMLC_*` environment variables required by XGBoost's
+  Collective communication layer (`DMLC_TRACKER_URI`, `DMLC_TRACKER_PORT`,
+  `DMLC_TASK_ID`, `DMLC_NUM_WORKER`).
+- Providing the rank-0 pod with the tracker address so user code can start a
+  `RabitTracker` for worker coordination.
+- Supporting both CPU and GPU training workloads.
+
+The built-in runtime is called `xgboost-distributed` and uses the container image
+`ghcr.io/kubeflow/trainer/xgboost-runtime:latest`, which includes XGBoost with
+CUDA 12 support, NumPy, and scikit-learn.
+
+### Worker Count
+
+The total number of XGBoost workers is calculated as:
+
+```text
+DMLC_NUM_WORKER = numNodes × workersPerNode
+```
+
+- **CPU training**: 1 worker per node. Each worker uses OpenMP to parallelize
+  across all available CPU cores.
+- **GPU training**: 1 worker per GPU. The GPU count is derived from
+  `resourcesPerNode` limits in the TrainJob.
+
+---
+
+## Further Information
+
+For comprehensive documentation including complete training examples (Python SDK
+and kubectl YAML), best practices (`QuantileDMatrix`, early stopping,
+checkpointing, logging), and common issues, see the XGBoost documentation:
+
+**[Distributed XGBoost on Kubernetes — XGBoost Tutorial](https://xgboost.readthedocs.io/en/latest/tutorials/kubernetes.html)**