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Description

Amazon EC2 Trn2 instances, equipped with AWS Trainium2 chips, are specifically designed to deliver exceptional performance in the training of generative AI models, such as large language and diffusion models. Users can experience cost savings of up to 50% in training expenses compared to other Amazon EC2 instances. These Trn2 instances can accommodate as many as 16 Trainium2 accelerators, boasting an impressive compute power of up to 3 petaflops using FP16/BF16 and 512 GB of high-bandwidth memory. For enhanced data and model parallelism, they are built with NeuronLink, a high-speed, nonblocking interconnect, and offer a substantial network bandwidth of up to 1600 Gbps via the second-generation Elastic Fabric Adapter (EFAv2). Trn2 instances are part of EC2 UltraClusters, which allow for scaling up to 30,000 interconnected Trainium2 chips within a nonblocking petabit-scale network, achieving a remarkable 6 exaflops of compute capability. Additionally, the AWS Neuron SDK provides seamless integration with widely used machine learning frameworks, including PyTorch and TensorFlow, making these instances a powerful choice for developers and researchers alike. This combination of cutting-edge technology and cost efficiency positions Trn2 instances as a leading option in the realm of high-performance deep learning.

Description

Amazon SageMaker HyperPod is a specialized and robust computing infrastructure designed to streamline and speed up the creation of extensive AI and machine learning models by managing distributed training, fine-tuning, and inference across numerous clusters equipped with hundreds or thousands of accelerators, such as GPUs and AWS Trainium chips. By alleviating the burdens associated with developing and overseeing machine learning infrastructure, it provides persistent clusters capable of automatically identifying and rectifying hardware malfunctions, resuming workloads seamlessly, and optimizing checkpointing to minimize the risk of interruptions — thus facilitating uninterrupted training sessions that can last for months. Furthermore, HyperPod features centralized resource governance, allowing administrators to establish priorities, quotas, and task-preemption rules to ensure that computing resources are allocated effectively among various tasks and teams, which maximizes utilization and decreases idle time. It also includes support for “recipes” and pre-configured settings, enabling rapid fine-tuning or customization of foundational models, such as Llama. This innovative infrastructure not only enhances efficiency but also empowers data scientists to focus more on developing their models rather than managing the underlying technology.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

AWS Trainium Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
AWS Batch Yes 
AWS Deep Learning AMIs Yes 
AWS EC2 Trn3 Instances No 
AWS Neuron Yes 
Amazon EC2 Yes 
Amazon EC2 Capacity Blocks for ML Yes 
Amazon EC2 G5 Instances Yes 
Amazon EC2 Inf1 Instances Yes 
Amazon EC2 P5 Instances Yes 
Amazon EC2 Trn1 Instances Yes 
Amazon EKS Yes 
Amazon Elastic Container Service (Amazon ECS) Yes 
Datadog Yes 
Domino Enterprise AI Platform Yes 
PyTorch Yes 
Ray Yes 
TensorFlow Yes 

Integrations

AWS Trainium Yes 
Amazon SageMaker Yes 
Amazon Web Services (AWS) Yes 
AWS Batch No 
AWS Deep Learning AMIs No 
AWS EC2 Trn3 Instances Yes 
AWS Neuron No 
Amazon EC2 No 
Amazon EC2 Capacity Blocks for ML No 
Amazon EC2 G5 Instances No 
Amazon EC2 Inf1 Instances No 
Amazon EC2 P5 Instances No 
Amazon EC2 Trn1 Instances No 
Amazon EKS No 
Amazon Elastic Container Service (Amazon ECS) No 
Datadog No 
Domino Enterprise AI Platform No 
PyTorch No 
Ray No 
TensorFlow No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours Yes 
Live Rep (24/7) Yes 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person No 

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/ec2/instance-types/trn2/

Vendor Details

Company Name

Amazon

Founded

1994

Country

United States

Website

aws.amazon.com/sagemaker/ai/hyperpod/

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

Machine Learning

Deep Learning No 
ML Algorithm Library No 
Model Training No 
Natural Language Processing (NLP) No 
Predictive Modeling No 
Statistical / Mathematical Tools No 
Templates No 
Visualization No 

Alternatives

Alternatives

Tinker Reviews

Tinker

Thinking Machines Lab
AWS Trainium Reviews

AWS Trainium

Amazon Web Services
AWS Neuron Reviews

AWS Neuron

Amazon Web Services