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GPU as a Service

Accelerate machine learning, deep learning and data processing using a single GPU as a Service solution, for faster and scalable AI-based applications

Finally, Accessible GPUs
For Everyone

GPUs play an important role in improving performance and scalability. However, GPUs are commonly under-utilized due to inefficient resource allocation, data bottlenecks, complicated DevOps and limited support for use-cases beyond deep learning. In a cloud-native era, with compute-intensive technologies like deep learning and video processing, data scientists need a platform that enables GPU as a service, based on open source technologies, with lower costs and supporting hybrid workloads (multi-cloud and/or on-premises).

Iguazio provides an end-to-end MLOps environment for both training and inference, and helps customers use their GPU investments efficiently, saving heavy compute costs, simplifying complex infrastructure, and improving performance.

For training with heavy workloads, Iguazio users can:

  • Run experiments with GPU resources attached, along with full resource control.
  • Assign GPUs to training engines like Spark or Horovod or to a Jupyter notebook, all within a simple UI.
  • Automatically free up resources with the scale to zero option, which triggers when a Jupyter notebook with assigned GPUs is idle for a certain amount of time.

For serving, Iguazio users can:

  • Scale up when the workload demands, and easily release GPU resources to scale down on-demand.
  • Simplify GPU management with out-of-the-box GPU monitoring reports on both the cluster and the application level.
Sharing GPU Resources Efficiently

Sharing GPU Resources Efficiently

GPUs sit idle while data scientists develop code, and when they are eventually needed there isn’t enough of them. As a result, heavy expenses are spent on unutilized GPUs, while not enough GPUs are used when running jobs. Iguazio provides a managed shared pool of resources and auto-scales GPUs as needed for maximum utilization.

Network Optimization

Cutting DevOps Efforts

Iguazio provides a fully managed platform with pre-installed services, built-in GPU enablement and serverless automation, all managed with a friendly UI. This cuts months spent on DevOps, enabling developers to focus on code as opposed to plumbing.

Unblocking Data Bottlenecks

Unblocking Data Bottlenecks

Typical data intensive applications require 10s or 100s of terabytes and lack of appropriate integration blocks data scientists from enjoying the benefits of GPUs. Iguazio streams data in chunks directly into GPUs, using SSDs at in-memory speeds and low latency access.

Deployed Anywhere

A Cloud Experience at the Edge

Many businesses use GPUs at the edge to avoid the cloud’s heavy costs, but suffer from a poor user experience. Iguazio’s GPU as a Service runs in multicloud, edge and on-premises environments, providing a friendly cloud experience anywhere.

Data Science For Financial Services
Data Science For Financial Services

Benefits

Zero Operations

Zero Operations

Collaborative Environment

Collaborative Environment

Performance at Scale

Performance at Scale

Maximum GPU Utilization

Maximum GPU Utilization

Flexible Deployment

Flexible Deployment

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Data Science Platform Tutorials

Platform Overview

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Data Science Platform Documentation

Documentation

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Maximize your GPU Investments

Learn how you can improve performance, save compute costs and simplify GPU management with the Iguazio MLOps Platform