AWS Machine Learning Blog

OCTOBER 5, 2023

In this post, we walk through how to fine-tune Llama 2 on AWS Trainium , a purpose-built accelerator for LLM training, to reduce training times and costs. We review the fine-tuning scripts provided by the AWS Neuron SDK (using NeMo Megatron-LM), the various configurations we used, and the throughput results we saw.

AWS 128

AWS Machine Learning Machine Learning Deep Learning 128

AWS Machine Learning Blog

APRIL 29, 2024

For AWS and Outerbounds customers, the goal is to build a differentiated machine learning and artificial intelligence (ML/AI) system and reliably improve it over time. First, the AWS Trainium accelerator provides a high-performance, cost-effective, and readily available solution for training and fine-tuning large models.

AWS 127

AWS ML ML Python 127

AWS Machine Learning Blog

MAY 1, 2024

Llama2 by Meta is an example of an LLM offered by AWS. To learn more about Llama 2 on AWS, refer to Llama 2 foundation models from Meta are now available in Amazon SageMaker JumpStart. Virginia) and US West (Oregon) AWS Regions, and most recently announced general availability in the US East (Ohio) Region.

AWS 125

AWS ML ML Clustering 125

AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. We’ll outline how we cost-effectively (3.2 billion in Pythia.

AWS 127

AWS Machine Learning Machine Learning Deep Learning 127

AWS Machine Learning Blog

JANUARY 24, 2024

We demonstrate how to build an end-to-end RAG application using Cohere’s language models through Amazon Bedrock and a Weaviate vector database on AWS Marketplace. Additionally, you can securely integrate and easily deploy your generative AI applications using the AWS tools you are already familiar with.

AWS 144

AWS Database AI AI 144

AWS Machine Learning Blog

NOVEMBER 30, 2023

Building foundation models (FMs) requires building, maintaining, and optimizing large clusters to train models with tens to hundreds of billions of parameters on vast amounts of data. SageMaker HyperPod integrates the Slurm Workload Manager for cluster and training job orchestration.

Clustering 123

Clustering AWS Machine Learning Machine Learning 123

AWS Machine Learning Blog

JUNE 11, 2024

In this post, we describe the scale of our AI offerings, the challenges with diverse AI workloads, and how we optimized mixed AI workload inference performance with AWS Graviton3 based c7g instances and achieved 20% throughput improvement, 30% latency reduction, and reduced our cost by 25–30%.

Machine Learning 117

Machine Learning Machine Learning AWS Natural Language Processing 117

AWS Machine Learning Blog

FEBRUARY 1, 2023

In late 2022, AWS announced the general availability of Amazon EC2 Trn1 instances powered by AWS Trainium —a purpose-built machine learning (ML) accelerator optimized to provide a high-performance, cost-effective, and massively scalable platform for training deep learning models in the cloud. 32xlarge instances.

AWS 98

AWS Clustering Deep Learning Deep Learning 98

AWS Machine Learning Blog

APRIL 29, 2024

Close collaboration with AWS Trainium has also played a major role in making the Arcee platform extremely performant, not only accelerating model training but also reducing overall costs and enforcing compliance and data integrity in the secure AWS environment. Our cluster consisted of 16 nodes, each equipped with a trn1n.32xlarge

AWS 125

AWS Clustering ML ML 125

AWS Machine Learning Blog

MARCH 15, 2024

Therefore, ML creates challenges for AWS customers who need to ensure privacy and security across distributed entities without compromising patient outcomes. Solution overview We deploy FedML into multiple EKS clusters integrated with SageMaker for experiment tracking. You can also download these models from the website.

AWS 136

AWS ML ML Machine Learning 136

Flipboard

FEBRUARY 16, 2023

Modern model pre-training often calls for larger cluster deployment to reduce time and cost. In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. The following diagram shows an example.

Clustering 131

Clustering AWS Deep Learning Deep Learning 131

AWS Machine Learning Blog

MAY 31, 2024

In this blog post and open source project , we show you how you can pre-train a genomics language model, HyenaDNA , using your genomic data in the AWS Cloud. Amazon SageMaker Amazon SageMaker is a fully managed ML service offered by AWS, designed to reduce the time and cost associated with training and tuning ML models at scale.

AWS 117

AWS ML ML Machine Learning 117

AWS Machine Learning Blog

APRIL 7, 2023

To reduce the barrier to entry of ML at the edge, we wanted to demonstrate an example of deploying a pre-trained model from Amazon SageMaker to AWS Wavelength , all in less than 100 lines of code. In this post, we demonstrate how to deploy a SageMaker model to AWS Wavelength to reduce model inference latency for 5G network-based applications.

AWS 101

AWS Clustering ML ML 101

AWS Machine Learning Blog

JANUARY 17, 2024

Today, we’re excited to announce the availability of Llama 2 inference and fine-tuning support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. In this post, we demonstrate how to deploy and fine-tune Llama 2 on Trainium and AWS Inferentia instances in SageMaker JumpStart.

AWS 131

AWS Python Machine Learning Machine Learning 131

AWS Machine Learning Blog

NOVEMBER 30, 2023

The number of companies launching generative AI applications on AWS is substantial and building quickly, including adidas, Booking.com, Bridgewater Associates, Clariant, Cox Automotive, GoDaddy, and LexisNexis Legal & Professional, to name just a few. Innovative startups like Perplexity AI are going all in on AWS for generative AI.

AWS 145

AWS AI AI ML 145

AWS Machine Learning Blog

APRIL 1, 2024

Distributed model training requires a cluster of worker nodes that can scale. In this blog post, AWS collaborates with Meta’s PyTorch team to discuss how to use the PyTorch FSDP library to achieve linear scaling of deep learning models on AWS seamlessly using Amazon EKS and AWS Deep Learning Containers (DLCs).

Clustering 131

Clustering AWS ML ML 131

AWS Machine Learning Blog

JULY 17, 2023

As described in the AWS Well-Architected Framework , separating workloads across accounts enables your organization to set common guardrails while isolating environments. Organizations with a multi-account architecture typically have Amazon Redshift and SageMaker Studio in two separate AWS accounts.

Clustering 93

Clustering AWS ML ML 93

AWS Machine Learning Blog

SEPTEMBER 7, 2023

Since then, this feature has been integrated into many of our managed Amazon Machine Images (AMIs), such as the Deep Learning AMI and the AWS ParallelCluster AMI. Prerequisites To simplify reproducing the entire stack from this post, we use a container that has all the required tooling (aws cli, eksctl, helm, etc.) already installed.

Clustering 124

Clustering AWS Machine Learning Machine Learning 124

AWS Machine Learning Blog

MARCH 11, 2025

The MoE architecture allows activation of 37 billion parameters, enabling efficient inference by routing queries to the most relevant expert clusters. By integrating this model with Amazon SageMaker AI , you can benefit from the AWS scalable infrastructure while maintaining high-quality language model capabilities.

AWS 78

AWS ML ML Natural Language Processing 78

AWS Machine Learning Blog

MAY 23, 2024

By distributing experts across workers, expert parallelism addresses the high memory requirements of loading all experts on a single device and enables MoE training on a larger cluster. The following figure offers a simplified look at how expert parallelism works on a multi-GPU cluster.

Clustering 132

Clustering AWS Deep Learning Deep Learning 132

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. Prerequisites To continue with the examples in this post, you need to create the required AWS resources.

ML 123

ML ML AWS Data Warehouse 123

AWS Machine Learning Blog

MAY 30, 2023

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. SageMaker supports various data sources and access patterns, distributed training including heterogenous clusters, as well as experiment management features and automatic model tuning.

AWS 95

AWS Deep Learning Deep Learning ML 95

AWS Machine Learning Blog

APRIL 18, 2024

It’s straightforward to deploy in your AWS account. Prerequisites You need to have an AWS account and an AWS Identity and Access Management (IAM) role and user with permissions to create and manage the necessary resources and components for this application. Everything you need is provided as open source in our GitHub repo.

AWS 138

AWS Analytics Analytics AI 138

AWS Machine Learning Blog

APRIL 16, 2024

IAM role – SageMaker requires an AWS Identity and Access Management (IAM) role to be assigned to a SageMaker Studio domain or user profile to manage permissions effectively. Create database connections The built-in SQL browsing and execution capabilities of SageMaker Studio are enhanced by AWS Glue connections. or later image versions.

SQL 126

SQL AWS Database Data Scientist 126

AWS Machine Learning Blog

MARCH 6, 2023

These factors require training an LLM over large clusters of accelerated machine learning (ML) instances. In the past few years, numerous customers have been using the AWS Cloud for LLM training. We recommend working with your AWS account team or contacting AWS Sales to determine the appropriate Region for your LLM workload.

AWS 106

AWS Clustering ML ML 106

AWS Machine Learning Blog

JUNE 7, 2023

Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch. We developed an AWS Batch workshop that illustrates the steps to set up the distributed training cluster with AWS Batch. billion-parameter model using the wikicorpus-en dataset. Pre-training of a 1.5-billion-parameter

AWS 94

AWS Clustering Deep Learning Deep Learning 94

AWS Machine Learning Blog

DECEMBER 15, 2023

Prerequisites To implement this solution, complete the following prerequisites: Have AWS Cloud admin access with an AWS Identity and Access Management (IAM) user with permissions required to complete the integration. Enter a connection name such as demo and choose your desired Amazon DocumentDB cluster. Choose Add connection.

Machine Learning 122

Machine Learning Machine Learning AWS ML 122

AWS Machine Learning Blog

OCTOBER 9, 2024

The service, which was launched in March 2021, predates several popular AWS offerings that have anomaly detection, such as Amazon OpenSearch , Amazon CloudWatch , AWS Glue Data Quality , Amazon Redshift ML , and Amazon QuickSight. To use this feature, you can write rules or analyzers and then turn on anomaly detection in AWS Glue ETL.

AWS 95

AWS ML ML Data Quality 95

AWS Machine Learning Blog

APRIL 25, 2024

We provide a comprehensive guide on how to deploy speaker segmentation and clustering solutions using SageMaker on the AWS Cloud. Solution overview Amazon Transcribe is the go-to service for speaker diarization in AWS. Make sure the AWS account has a service quota for hosting a SageMaker endpoint for an ml.g5.2xlarge instance.

AWS 123

AWS ML ML Python 123

AWS Machine Learning Blog

MAY 16, 2024

The main AWS services used are SageMaker, Amazon EMR , AWS CodeBuild , Amazon Simple Storage Service (Amazon S3), Amazon EventBridge , AWS Lambda , and Amazon API Gateway. Using SageMaker in conjunction with various AWS services has given us many advantages in developing and operating our services.

AWS 127

AWS ML ML Deep Learning 127

AWS Machine Learning Blog

APRIL 19, 2024

We used AWS services including Amazon Bedrock , Amazon SageMaker , and Amazon OpenSearch Serverless in this solution. In this series, we use the slide deck Train and deploy Stable Diffusion using AWS Trainium & AWS Inferentia from the AWS Summit in Toronto, June 2023 to demonstrate the solution.

AWS 127

AWS ML ML Database 127

AWS Machine Learning Blog

NOVEMBER 15, 2024

Prerequisites To try out this solution using SageMaker JumpStart, you need the following prerequisites: An AWS account that will contain all of your AWS resources. An AWS Identity and Access Management (IAM) role to access SageMaker. These models are released under different licenses designated by their respective sources.

ML 110

ML ML Python AWS 110

AWS Machine Learning Blog

JANUARY 6, 2023

Many AWS media and entertainment customers license IMDb data through AWS Data Exchange to improve content discovery and increase customer engagement and retention. We downloaded the data from AWS Data Exchange and processed it in AWS Glue to generate KG files. Background. Prerequisites.

AWS 101

AWS ML ML Machine Learning 101

AWS Machine Learning Blog

JULY 11, 2023

When it comes to ML, this restricts data scientists from downloading any package from public repositories like PyPI , Anaconda , or Conda-Forge. The high-level steps to implement the solution are as follows Set up a virtual private cloud (VPC) with no internet access using an AWS CloudFormation template.

AWS 77

AWS Python ML ML 77

AWS Machine Learning Blog

JUNE 7, 2023

The model weights are available to download, inspect and deploy anywhere. SageMaker Training provisions compute clusters with user-defined hardware configuration and code. TII used transient clusters provided by the SageMaker Training API to train the Falcon LLM, up to 48 ml.p4d.24xlarge

Clustering 101

Clustering Machine Learning Machine Learning AWS 101

AWS Machine Learning Blog

OCTOBER 23, 2024

The high-level steps are as follows: Set up an Amazon SageMaker Studio environment with the necessary AWS Identity and Access Management (IAM) policies. The process involves the following steps: Download the training and validation data, which consists of PDFs from Uber and Lyft 10K documents. Open SageMaker Studio.

AWS 84

AWS Artificial Intelligence Artificial Intelligence Machine Learning 84

AWS Machine Learning Blog

MAY 25, 2023

We use a combination of different AWS services, open-source foundation models ( FLAN-T5 XXL for text generation and GPT-j-6B for embeddings) and packages such as LangChain for interfacing with all the components and Streamlit for building the bot frontend. AWS Identity and Access Management roles and policies for access management.

AWS 98

AWS Clustering Python ML 98