AI Acceleration

Accelerate AI-driven business and AI model training and inference with Alibaba Cloud GPU technology

Overview

The development of machine learning technology for AI services relies on data, algorithms, and computing power. Alibaba Cloud AI Acceleration solution leverages leading dataset and GPU accelerators to orchestrate data-intensive applications, optimize utilization of GPU instances, and speed up AI model training and inference tasks. You can also use Alibaba Cloud's customizable cloud-native AI training platform to tailor to your business needs and improve the efficiency for AI infrastructure and daily O&M.

Solution Highlights

  • Cost-effective Atomic Acceleration

    Speed up AI model training and inferences with the fastest GPU Accelerator, AIACC, according to Stanford DAWN Deep Learning Benchmark to accelerate AI training models by 70% and inference by 2-3 times and reduce cost

  • Highly Efficient GPU Usage

    Improve the utilization rate of all GPUs in the cluster with GPU cluster scheduling that shares the resources of the same GPU, and eradicate interference between applications with the GPU isolation mechanism

  • CNCF-Recognized Dataset Accelerator

    Resolve high access latency, complex parallel access of multiple data sources, and weak data isolation during AI training with Fluid, the Distributed Dataset Orchestrator and Accelerator for data-intensive applications

  • Customizable AI Training Platform

    Build and customize your cloud-native AI training platform with a console for development and O&M by using Alibaba Cloud-Native AI Suite to improve AI engineering efficiency in all stages of deep learning

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How It Works

Alibaba AI Acceleration solution include the Cloud-Native AI Suite layer, AI Job Scheduling layer, AI Data Acceleration layer, AI Computing Acceleration Engine layer, Resource Scheduling layer, and Computing Resource layer. Each layer offers specific acceleration solutions based on AI application characteristics in various scenarios.
Cloud-Native AI Suite: Simplify operations with underlying services and components, monitor GPU resource utilization, and collect and analyze logs of AI jobs. You can use command lines to submit AI jobs and check the schedule of model training.
AI Job Scheduling: Schedule AI jobs to accelerate models training, increase GPU utilization, and save costs.
AIACC: Leverage deep learning frameworks such as Tensoflow, PyTorch, MXNet, and Caffe to categorize images and achieve image recognition, Click-Through Rate prediction, Neuro-Linguistic Programming, speech recognition, face recognition, etc.
Data Acceleration: Adopt the architecture with separated storage and computing resources with data stored in different devices from on-premises and model training executed on Alibaba Cloud. Data are stored in small files and warmed up before model training to improve transmission and training efficiency.
Resource Scheduling: Increase GPU utilization and save costs by sharing GPU resources with multiple AI jobs and running single AI jobs on multiple GPUs for distributed model training.
Computing Resources-GPU: Accelerate AI training and inference.

Use Cases

Image Recognition

MXNet + SyncBN Distribution training. Performance increased by 50%.

NLP

Natural language processing distribution training. Performance increased 6 times.

Speech Recognition

Speech transform to words. Text sent to students and shown on screen when students are speaking.

CTR (Click-Through Rate)

Distribution training. Performance increased 6.6 times.

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Special Offers

Elastic GPU Service

Powerful parallel computing capabilities based on GPU technology

15% OFF

• Deep Learning

• Video Processing

• Scientific Computing

$9164.190/ Year

Cloud Enterprise Network

A hybrid and distributed global network ideal for enterprise users with high demand on network coverage

Free Trial

• Free for 1 Month Within 5M

• High-Quality Communication

• Route Convergence in Seconds

Free Trial

Container Registry

An enterprise-class platform that provides secure management and efficient distribution of container images

25% OFF

• ACR EE Advanced Edition

• Enterprise-Level Security

• Accelerated Global Distribution

$1,017.00/ Year

Machine Learning Platform for AI

An end-to-end platform for data mining and analysis

• Visualized Interface

• 100+ Algorithm Components

• Powerful Computing Capability

From $0.16/ Task/ Hour

Security and Compliance

We are committed to providing stable, reliable, secure, and compliant cloud computing infrastructure services across major jurisdictions around the world.
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  • CSA STAR
  • ISO 27001
  • SOC2 Type II Report
  • C5
  • MLPS 2.0
  • MTCS

Customer Success Stories

"Sina Weibo is continuously saving costs by using Function Compute to process image files. We do not need to reserve a large number of idle machine resources to handle traffic surges during peak hours any more. Our engineers do not need to waste time on infrastructure management, such as monitoring server status. Instead, they can focus on working with our product teams to increase business value."

Sina Weibo is a social media platform that is aimed at helping users build better connections. It provides a simple and unprecedented way for users to create posts in real time. Users can interact with each other and connect with people all over the world through viral yet informative content.

Related Resources

Whitepaper

AI Acceleration Whitepaper

This whitepaper is a complete guide on the mechanism of the AI infrastructure layer and how it supports AI acceleration.

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Best Practice

cGPU (GPU Sharing) for AI Inference

This document introduces the process about how to deploy the cGPU component in the ACK environment.

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Best Practice

AI Acceleration Demo - AIACC + ACK (Tensorflow)

This demo helps you train ImageNet data with Tensorflow 2.4 by leveraging AIACC, an AI acceleration engine running on ACK (K8s) to speed up your AI training.

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Best Practice

AI Acceleration Demo - AIACC (TensorFlow)

This solution helps you train ImageNet with TensorFlow 2.4 by leveraging AIACC an AI Acceleration Engine, to speed up your AI training.

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Best Practice

AI Acceleration Demo - AIACC + ACK (Pytorch)

This solution helps you train ImageNet data with Pytorch 1.9 by leveraging AIACC, an AI Acceleration Engine running on ACK (K8s), to speed up your AI training.

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Best Practice

AI Acceleration Demo - AIACC (Pytorch)

This solution helps you train ImageNet with Pytorch 1.7/1.9 by leveraging AIACC, an AI Acceleration Engine, to speed up your AI training.

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