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GPU Optimization with Run:ai Atlas
Introduction Overview of Run:ai Atlas platform Reference Architecture of Run:ai Atlas on Dell infrastructure Use case validation for Run:ai Atlas
Overview
Dashboard and analytics
Training and Interactive build
GPU fractions
Hyperparameter optimization
Deployment Considerations Why Dell Technologies? Dell Technologies services and support The Dell Technologies Customer Solutions Center Conclusion References

Overview

Overview

  • We deployed the Run:ai Atlas platform in a Kubernetes cluster on the Symcloud Platform. The following table describes the hardware and software of the configuration used to validate this design:

    Table 1. Validation setup

    Installation type

    Classic (SaaS)

    Self-hosted

    Servers

    4 x PowerEdge R7525 servers each with two NVIDIA A100 GPUs

    4 x PowerEdge R7525 servers (3 servers with 2 NVIDIA A100 GPUs and 1 server with 1 NVIDIA A30 GPU)

    Container orchestration

    Symcloud Platform version 5.3.11

    Symcloud Platform version 5.4.2

    Storage

    Symcloud Cloud Native Storage

    Symcloud Cloud Native Storage

    Network switches

    Dell S5248F-ON (for workload and management)

    Dell S4148T-ON OOB

    Dell S5248F-ON (for workload and management) 

    Dell S4148T-ON OOB 

     

    NVIDIA GPU operator

    Version 22.2.9

    Version 22.2.9

    Run:ai Atlas

    Cluster version 2.8.14

    Control plane version 2.9.5, Cluster version 2.8.14

    Dell Validated Design for Analytics—Data Lakehouse incorporates Symcloud Platform version 5.3.x. We ran our validation of the Run:ai Classic (SaaS) deployment on this version. See the Dell Validated Design for Analytics—Data Lakehouse Design Guide for more information about the reference architecture. We ran the Run:ai Atlas self-hosted deployment and validation was on a newer version of Symcloud Platform, which as of publication of this document is yet to be validated as part of the Dell Validated Design for Analytics – Data Lakehouse.