Example High-performance Jobs for ONTAP AI Deployments
Contributors
This section includes examples of various high-performance jobs that can be executed when Kubernetes is deployed on an ONTAP AI pod.
-
PDF of this doc site
Collection of separate PDF docs
Creating your file...
This may take a few minutes. Thanks for your patience.
Your file is ready
- NetApp Solutions Documentation
-
Artificial Intelligence
-
AI Converged Infrastructures
-
ONTAP AI with NVIDIA
-
EF-Series AI with NVIDA
- BeeGFS with NetApp E-Series Reference Architecture
- Deploying IBM Spectrum Scale with NetApp E-Series Storage
- NetApp ONTAP and Lenovo ThinkSystem SR670 for AI and ML Model Training Workloads
- NetApp AFF A800 and Fujitsu Server PRIMERGY GX2570 M5 for AI and ML Model Training Workloads
-
-
Data Pipelines, Data Lakes and Management
- NetApp StorageGRID Data Lake for Autonomous Driving Workloads
-
NetApp AI Control Plane
- Introduction
- Concepts and Components
- Hardware and Software Requirements
- Kubernetes Deployment
-
NetApp Trident Deployment and Configuration
- Kubeflow Deployment
-
Example Kubeflow Operations and Tasks
- Apache Airflow Deployment
- Example Apache Airflow Workflows
- Example Trident Operations
-
Example High-performance Jobs for ONTAP AI Deployments
- Performance Testing
- Conclusion
-
MLRun Pipeline
- NetApp DataOps Toolkit
-
Use Cases
-
Sentiment analysis with NetApp AI
-
Distributed training in Azure - Click-Through Rate Prediction
- Introduction
- Technology overview
- Software requirements
- Cloud resource requirements
- Click-through rate prediction use case summary
-
Setup
-
Click through rate prediction data processing and model training
- Libraries for data processing and model training
- Load Criteo Click Logs day 15 in Pandas and train a scikit-learn random forest model
- Load Day 15 in Dask and train a Dask cuML random forest model
- Monitor Dask using native Task Streams dashboard
- Training time comparison
- Monitor Dask and RAPIDS with Prometheus and Grafana
- Dataset and Model Versioning using NetApp DataOps Toolkit
- Jupyter notebooks for Reference
- Conclusion
- Where to find additional information
- Version history
-
Distributed Azure training - Lane detection
-
Hybrid Cloud AI Operating System with Data Caching
- Moving Data from a Big Data Environment to an AI Environment
-
AI Inferencing at the Edge - NetApp with Lenovo ThinkSystem - Solution Design
-
Conversational AI using NVIDIA
-
NetApp Orchestration Solution with Run:AI
- Solution Overview
- Solution Technology
-
Optimal Cluster and GPU Utilization with Run AI
- Run AI Installation
- Run AI Dashboards and Views
- Creating Projects for Data Science Teams and Allocating GPUs
- Submitting Jobs in Run AI CLI
- Achieving High Cluster Utilization
- Fractional GPU Allocation for Less Demanding or Interactive Workloads
- Achieving High Cluster Utilization with Over-uota GPU Allocation
- Basic Resource Allocation Fairness
- Over-Quota Fairness
- Saving Data to a Trident-Provisioned PersistentVolume
- Conclusion
- Testing Details for Section 4.8
- Testing Details for Section 4.9
- Testing Details for Section 4.10
- Where to Find Additional Information
- NetApp ONTAP AI for Autonomous Driving Workloads Solution Design
- NetApp ONTAP AI Reference Architecture for Healthcare: Diagnostic Imaging
- NetApp ONTAP AI Reference Architecture for Financial Services Workloads
- AI Deployment with NetApp E-Series and BeeGFS
- Quantum StorNext with NetApp E-Series Systems Design Guide
- Quantum StorNext with NetApp E-Series Systems Deployment Guide
-
-
-
Modern Data Analytics
-
Best practices for Confluent Kafka
-
NetApp hybrid cloud data solutions - Spark and Hadoop based on customer use cases
- Solution overview
- Data fabric powered by NetApp for big data architecture
- Hadoop data protection and NetApp
- Overview of Hadoop data protection use cases
- Use case 1 - Backing up Hadoop data
- Use case 2 - Backup and disaster recovery from the cloud to on-premises
- Use case 3 - Enabling DevTest on existing Hadoop data
- Use case 4 - Data protection and multicloud connectivity
- Use case 5 - Accelerate analytic workloads
- Conclusion
- Different Solutions for Different Analytics Strategies Solution Brief
- NetApp StorageGRID with Splunk SmartStore
- NetApp E-Series E5700 and Splunk Enterprise
- Apache Spark Workload with NetApp Storage Solution (Deployment Guide)
-
-
Virtualization
- Get Started With NetApp and VMware
- ONTAP tools for VMware vSphere - Product Security
-
VMware Virtualization for ONTAP
-
VMware for Public Cloud
-
VMware Hybrid Cloud Use Cases
-
Demos and Tutorials
-
Blogs
-
Virtual Desktops
-
Virtual Desktop Services (VDS)
-
Hybrid Cloud VDI with NetApp Virtual Desktop Service
- Use Cases
- NetApp Virtual Desktop Service Overview
- NetApp HCI Overview
- NVIDIA Licensing
- Deployment
- Hybrid Cloud Environment
- Single Server Load Test with Login VSI
- Management Portal
- User Management
- Workspace Management
- Application Management
- ONTAP features for Virtual Desktop Service
- Data Management
- Operation Management
- Tools and Logs
- GPU Considerations
- Solutions for industry
- Conclusion
- Where to Find Additional Information
- ESG Technical Validation: VDI at Enterprise Scale with NetApp Virtual Desktop Service
-
-
VMware Horizon
- Citrix Virtual Apps and Desktops
- FlexPod Desktop Virtualization Solutions
-
-
Containers
-
Red Hat OpenShift with NetApp
-
Red Hat Openshift Overview
-
NetApp Storage Systems Overview
-
NetApp Storage Integrations Overview
-
Advanced Configuration Options For OpenShift
-
Solution Validation and Use Cases
- Videos / Demos
- Additional Information
-
-
Google Anthos
-
-
Enterprise Applications and Databases
- SAP and SAP HANA
-
Oracle Database
-
Microsoft SQL Server
-
Hybrid Cloud Database Solutions with SnapCenter
-
Data Migration and Data Protection
-
Data Migration
-
Best-Practice Guidelines for NetApp XCP
- Introduction
- NetApp XCP
- Migration workflow
- File analytics
- Deployment steps
- Sizing guidelines
- Performance tuning
-
Customer scenarios
- Overview
- Data lake to ONTAP NFS
- High-performance computing to ONTAP NFS
- Using the XCP Data Mover to migrate millions of small files to flexible storage
- Using the XCP Data Mover to migrate large files
- Duplicate files
- Specific date-based scan and copy of data
- Creating a CSV file from SMB/CIFS share
- Data migration from 7-Mode to ONTAP
- CIFS data migration with ACLs From a source storage box to ONTAP
- Best practice guidelines and recommendations
- Troubleshooting
- Where to find additional information
- Version history
-
- Data Protection
-
-
Solution Automation
- FlexPod Solutions
- Legacy NetApp HCI Solutions
- Change Log
- About NetApp Solutions
- Legal Notices
Request doc changes
Edit this page
Learn how to contribute