Kubeflow 1.0: Machine Learning Platform Reaches Production

Introduction

On March 2, 2020, Kubeflow 1.0 was released, marking a significant milestone for machine learning on Kubernetes. This first major release provided a production-ready platform for developing, training, and deploying machine learning models at scale, bringing together the best practices from the ML community with Kubernetes’ orchestration capabilities.

Production-Ready ML Platform

• Stable APIs provide reliable interfaces for ML workflows and model management.
• Component maturity ensures production-grade reliability for training, serving, and experimentation.
• Comprehensive tooling covers the entire ML lifecycle from development to deployment.
• Kubernetes-native design leverages Kubernetes’ scalability and resource management.

Core Components

1. Kubeflow Pipelines enables building, deploying, and managing end-to-end ML workflows with reusable components.
2. Training Operators support distributed training for TensorFlow, PyTorch, MXNet, and XGBoost.
3. KServe (formerly KFServing) provides model serving with automatic scaling and canary deployments.
4. Kubeflow Notebooks offers Jupyter notebook environments for interactive ML development.
5. Katib provides automated hyperparameter tuning and neural architecture search.

Key Features

• Multi-framework support enables using TensorFlow, PyTorch, and other ML frameworks.
• Distributed training scales training jobs across multiple nodes and GPUs.
• Model serving provides production-ready serving with autoscaling and traffic splitting.
• Experiment tracking enables tracking and comparing ML experiments and model versions.
• Workflow orchestration manages complex ML pipelines with dependencies and retries.

Getting Started

kubectl apply -k "github.com/kubeflow/manifests/kfdef/kfctl_k8s_istio.v1.0.2.yaml"

Create a training job:

apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  name: mnist-train
spec:
  tfReplicaSpecs:
    Worker:
      replicas: 2
      template:
        spec:
          containers:
          - name: tensorflow
            image: tensorflow/tensorflow:latest
            command:
            - python
            - /opt/model.py

Summary

Kubeflow 1.0 represents a major achievement in bringing machine learning to Kubernetes, providing data scientists and engineers with the tools needed to build and deploy ML models at scale.