Streamlining Competitive Data Science at CERN: CubeFlow Integration for Machine Learning Challenges

Introduction

CERN, home to the Large Hadron Collider (LHC), generates unprecedented volumes of data through high-energy particle collisions. The LHC accelerates proton beams to near-light speeds, producing billions of particle interactions per second. This data deluge necessitates advanced machine learning (ML) solutions to filter, analyze, and interpret results efficiently. To address these challenges, CERN has developed a competitive ML platform integrated with CubeFlow, leveraging CNCF technologies to streamline data science workflows.

Technical Overview

CubeFlow and Its Role

CubeFlow serves as the core platform for CERN’s ML challenges, providing a scalable infrastructure for distributed training, model deployment, and collaborative experimentation. It integrates key CNCF components such as KubeFlow Pipelines (KFP), Katib for hyperparameter optimization, and Kserve for model serving, enabling seamless ML workflows within Kubernetes environments.

Key Features

• Distributed Training: Supports CPU, GPU (T4/V100/A100/H100/AMD), and ARM-based resources, ensuring flexibility for diverse computational needs.
• Security & Isolation: Implements OIDC authentication, custom controllers for token injection, and Kubernetes resource quotas to safeguard sensitive data and prevent resource overuse.
• Collaborative Workflows: Provides Jupyter Notebook environments with mounted storage, allowing users to iterate and submit containerized code for evaluation.
• Scalable Pipelines: Automates data processing, model training, and scoring using KFP, with customizable scoring logic and leaderboard updates.

Application Workflow

Data Preparation

Challenge maintainers define datasets (via Docker images) and ground truth labels. Users download training and test data, with CubeFlow SDK managing input/output artifacts. Data is stored using KFP Data Sets or Persistent Volumes (PVs), supporting S3 artifact storage for large-scale datasets.

Code Execution

Users submit containerized code (Docker images or Jupyter Notebooks) for training and prediction. CubeFlow Pipelines orchestrate execution, ensuring isolation and resource allocation. For example, a user might train a random forest classifier on CSV data and generate predictions for evaluation.

Evaluation & Leaderboard

Challenge maintainers define scoring metrics (e.g., accuracy) and map outputs to KFP Data Sets. The platform automatically calculates scores, updates leaderboards, and provides user feedback, fostering competitive innovation.

Technical Challenges & Solutions

Authentication & Resource Management

• OIDC Integration: Custom controllers inject OIDC tokens, enabling secure access to CubeFlow APIs and storage systems.
• Resource Isolation: Argo and GitOps manage pipelines, while Kubernetes resource quotas prevent over-subscription of GPU/ARM resources.

Scalability & Hardware Diversity

CubeFlow supports ARM and AMD GPUs, with future plans to integrate Multi-Instance GPU (MIG) and Multi-Process Service (MPS) for enhanced utilization. The platform also enables bursting to public clouds via Q (MultiQ) for peak workloads.

Conclusion

CERN’s integration of CubeFlow addresses critical ML challenges in high-energy physics, offering a secure, scalable, and collaborative environment for data science. By leveraging CNCF technologies, the platform enables efficient processing of petabyte-scale datasets, from particle classification to anomaly detection. Future enhancements, including model registry integration and dynamic resource allocation, will further solidify its role in advancing scientific discovery through machine learning.