Principle:Kubeflow Kubeflow Train Model
| Knowledge Sources | |
|---|---|
| Domains | MLOps, Distributed Training, Model Development |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Train Model is the principle of executing model training workloads as managed, scalable, and trackable Kubernetes-native jobs that support distributed training across multiple nodes and accelerators.
Description
Model training is the compute-intensive core of the ML lifecycle, where algorithms learn from data to produce model artifacts. In production ML systems, training must go beyond a single-machine script execution to address scalability (training on large datasets across multiple GPUs or nodes), reproducibility (deterministic configurations and tracked outputs), fault tolerance (checkpointing and recovery from node failures), and resource efficiency (right-sized allocation and scheduling of expensive accelerator hardware).
This principle defines the disciplined practice of executing training workloads through a managed training orchestrator rather than ad-hoc script execution. The orchestrator handles pod scheduling, inter-node communication setup, distributed training framework initialization, failure detection and recovery, and completion reporting.
Within the Kubeflow ecosystem, the Trainer component (evolving toward Trainer V2.0 in the v1.11 roadmap) provides the TrainJob CRD as a unified API for submitting training jobs. The Trainer supports multiple distributed training frameworks (PyTorch, TensorFlow, MPI, XGBoost, JAX) and abstracts away the complexity of configuring distributed communication, rank assignment, and resource topology.
Usage
Apply this principle when:
- A model must be trained on datasets or architectures too large for a single machine or interactive notebook.
- Distributed training across multiple GPUs or nodes is required for acceptable training time.
- Training jobs must be reproducible, with tracked configurations, inputs, and outputs.
- Fault tolerance is needed for long-running training jobs (hours or days).
- Training must integrate into an automated pipeline as a scheduled or triggered step.
- Resource governance requires that training workloads be submitted through a managed scheduler.
Theoretical Basis
Model training as a managed process follows these stages:
Step 1: Training Configuration
- Define the model architecture and training algorithm.
- Specify the dataset source and any preprocessing requirements.
- Set hyperparameters (learning rate, batch size, epochs, optimizer settings).
- Determine the compute topology: number of nodes, GPUs per node, distributed strategy.
Step 2: Job Submission
- Package the training code, configuration, and dependencies into a container image or reference an existing runtime.
- Submit the training job specification to the training orchestrator.
- The orchestrator validates the specification and schedules the required pods.
Step 3: Distributed Execution
- The orchestrator initializes the distributed training environment (rank assignment, master address, communication backend).
- Worker pods execute the training loop, synchronizing gradients or parameters according to the chosen strategy (data parallelism, model parallelism, or hybrid).
- Checkpoints are periodically written to persistent storage.
Step 4: Monitoring and Fault Recovery
- The orchestrator monitors pod health and training progress.
- If a worker fails, the orchestrator may restart the failed pod and resume from the last checkpoint.
- Training metrics (loss, accuracy, throughput) are emitted for monitoring.
Step 5: Completion and Artifact Storage
- Upon completion, the final model artifacts are written to the configured output location.
- The training job status, logs, and metrics are recorded for downstream consumption.
- The trained model is ready for hyperparameter tuning evaluation, model registration, or direct serving.