Kubeflow training operator
The Kubeflow Training Operator is a Kubernetes operator that manages distributed and nondistributed machine learning training jobs through custom resources.
Designing Deep Learning Systems: A guide for software engineers
A Kubeflow controller that launches, monitors, and reconciles Kubernetes resources for machine learning training jobs in a specific framework.
- How it is used
- Runs containerized framework-specific training jobs as a Kubernetes-native backend for scalable, distributed model training.
- How deep it goes
- Explains CRD-driven reconciliation, pod creation, failover, installation, job YAML submission, monitoring, and wrapper-based integration.
- Examples
- Demonstrates a PyTorchJob YAML with Master and Worker replicas, environment variables, a Python MNIST command, and kubectl status queries.
- What you can do after
- Operate/configure it by installing a standalone operator, submitting framework job CRDs, and reading reconciled training status.
Distributed Machine Learning Patterns
The Kubeflow Training Operator is a Kubernetes operator that manages distributed and nondistributed machine learning training jobs through custom resources.
- How it is used
- Runs distributed or nondistributed machine-learning jobs on Kubernetes through custom resources.
- How deep it goes
- Defines custom resources, installs the operator, submits a TFJob, and checks Pods; it defers workflow orchestration.
- Examples
- Uses a local cluster, kubeflow namespace, TFJob manifest, two TensorFlow workers, and kubectl job and Pod listings.
- What you can do after
- Operate/configure distributed TensorFlow training on a local Kubernetes cluster using kubectl and TFJob manifests.
Read how Distributed Machine Learning Patterns explains Kubeflow training operator →
Related concepts
- Kubernetes operatoruses · A Kubernetes operator is a controller pattern that extends Kubernetes to automate application or service management by reconciling desired and actual state through the Kubernetes API.
- Kubeflowpart of · Kubeflow is an open-source platform for developing, deploying, and orchestrating machine-learning workflows on Kubernetes.
- custom objectuses · A Kubernetes custom object is an API object whose resource type and schema are defined by a custom resource definition or another extension mechanism.
- custom resource definitionuses · A CustomResourceDefinition is a Kubernetes API object that defines a custom resource type and its schema.
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