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.

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AWS Machine Learning Blog

JULY 2, 2024

AWS optimized the PyTorch torch.compile feature for AWS Graviton3 processors. the optimizations are available in torch Python wheels and AWS Graviton PyTorch deep learning container (DLC). The goal for the AWS Graviton team was to optimize torch.compile backend for Graviton3 processors.

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AWS Machine Learning Blog

JUNE 6, 2023

PyTorch is a machine learning (ML) framework that is widely used by AWS customers for a variety of applications, such as computer vision, natural language processing, content creation, and more. release, AWS customers can now do same things as they could with PyTorch 1.x 24xlarge with AWS PyTorch 2.0 on AWS PyTorch2.0

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AWS Machine Learning Blog

MAY 4, 2023

AWS has been innovating with purpose-built chips to address the growing need for powerful, efficient, and cost-effective compute hardware. You can use ml.trn1 and ml.inf2 compatible AWS Deep Learning Containers (DLCs) for PyTorch, TensorFlow, Hugging Face, and large model inference (LMI) to easily get started.

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AWS Machine Learning Blog

OCTOBER 10, 2023

Machine learning (ML), especially deep learning, requires a large amount of data for improving model performance. Customers often need to train a model with data from different regions, organizations, or AWS accounts. Federated learning (FL) is a distributed ML approach that trains ML models on distributed datasets.

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Flipboard

JUNE 26, 2023

These techniques utilize various machine learning (ML) based approaches. In this post, we look at how we can use AWS Glue and the AWS Lake Formation ML transform FindMatches to harmonize (deduplicate) customer data coming from different sources to get a complete customer profile to be able to provide better customer experience.

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AWS Machine Learning Blog

MAY 3, 2023

AWS, Arm, Meta and others helped optimize the performance of PyTorch 2.0 As a result, we are delighted to announce that AWS Graviton-based instance inference performance for PyTorch 2.0 times the speed for BERT, making Graviton-based instances the fastest compute optimized instances on AWS for these models. is up to 3.5

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AWS Machine Learning Blog

FEBRUARY 1, 2023

Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Many enterprise customers choose to deploy their deep learning workloads using Kubernetes—the de facto standard for container orchestration in the cloud.

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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%.

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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.

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AWS Machine Learning Blog

JULY 26, 2023

In this post, we show how you can run Stable Diffusion models and achieve high performance at the lowest cost in Amazon Elastic Compute Cloud (Amazon EC2) using Amazon EC2 Inf2 instances powered by AWS Inferentia2. versions on AWS Inferentia2 cost-effectively. You can run both Stable Diffusion 2.1 The Stable Diffusion 2.1

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AWS Machine Learning Blog

APRIL 27, 2023

Deep learning (DL) is a fast-evolving field, and practitioners are constantly innovating DL models and inventing ways to speed them up. Custom operators are one of the mechanisms developers use to push the boundaries of DL innovation by extending the functionality of existing machine learning (ML) frameworks such as PyTorch.

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AWS Machine Learning Blog

MAY 30, 2023

In 2021, we launched AWS Support Proactive Services as part of the AWS Enterprise Support plan. Since its introduction, we’ve helped hundreds of customers optimize their workloads, set guardrails, and improve the visibility of their machine learning (ML) workloads’ cost and usage.

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AWS Machine Learning Blog

AUGUST 26, 2024

AWS makes it possible for organizations of all sizes and developers of all skill levels to build and scale generative AI applications with security, privacy, and responsible AI. In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos.

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Mlearning.ai

JUNE 7, 2023

These Cores accelerate matrix operations and are specifically designed to boost deep learning training and inference performance. These Cores accelerate matrix operations and are specifically designed to boost deep learning training and inference performance. Inf1 accelerators can deliver up to 2.3x

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Flipboard

FEBRUARY 16, 2023

In October 2022, we launched Amazon EC2 Trn1 Instances , powered by AWS Trainium , which is the second generation machine learning accelerator designed by AWS. Trn1 instances are purpose built for high-performance deep learning model training while offering up to 50% cost-to-train savings over comparable GPU-based instances.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

With an aim to accelerate the localization of content workflows through machine learning, ZOO Digital engaged AWS Prototyping, an investment program by AWS to co-build workloads with customers. This S3 bucket was configured to emit an event when new files are detected within it, triggering an AWS Lambda function.

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AWS Machine Learning Blog

MARCH 8, 2023

This post shows a way to do this using Snowflake as the data source and by downloading the data directly from Snowflake into a SageMaker Training job instance. We create a custom training container that downloads data directly from the Snowflake table into the training instance rather than first downloading the data into an S3 bucket.

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AWS Machine Learning Blog

JUNE 13, 2023

In this post, we demonstrate how to deploy Falcon for applications like language understanding and automated writing assistance using large model inference deep learning containers on SageMaker. SageMaker large model inference (LMI) deep learning containers (DLCs) can help.

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AWS Machine Learning Blog

OCTOBER 30, 2024

Global Resiliency is a new Amazon Lex capability that enables near real-time replication of your Amazon Lex V2 bots in a second AWS Region. Additionally, we discuss how to handle integrations with AWS Lambda and Amazon CloudWatch after enabling Global Resiliency. We walk through the instructions to replicate the bot later in this post.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

For example, you might have acquired a company that was already running on a different cloud provider, or you may have a workload that generates value from unique capabilities provided by AWS. We show how you can build and train an ML model in AWS and deploy the model in another platform.

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AWS Machine Learning Blog

SEPTEMBER 13, 2024

Multimodal is a type of deep learning using multiple modalities of data, such as text, audio, or images. This feature is available in all AWS Regions where SageMaker is available. To learn more about deploying models on SageMaker, see Amazon SageMaker Model Deployment. gpu-py311-cu121-ubuntu20.04-sagemaker.

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AWS Machine Learning Blog

DECEMBER 12, 2023

The launch of ChatGPT and rise in popularity of generative AI have captured the imagination of customers who are curious about how they can use this technology to create new products and services on AWS, such as enterprise chatbots, which are more conversational. Optionally, deploy the application using AWS Amplify.

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AWS Machine Learning Blog

OCTOBER 4, 2024

You can download the generated images directly from the UI or check the image in your S3 bucket. We encourage you to explore the potential of building more agents like this fashion assistant by checking out the examples available on the aws-samples GitHub repository. Maira Ladeira Tanke is a Senior Generative AI Data Scientist at AWS.

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AWS Machine Learning Blog

APRIL 1, 2024

Machine learning (ML) research has proven that large language models (LLMs) trained with significantly large datasets result in better model quality. Solution overview In this post, we set up a compute cluster using Amazon EKS, which is a managed service to run Kubernetes in the AWS Cloud and on-premises data centers.

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AWS Machine Learning Blog

MAY 16, 2024

Therefore, we decided to introduce a deep learning-based recommendation algorithm that can identify not only linear relationships in the data, but also more complex relationships. However, it was necessary to upgrade the recommendation service to analyze each customer’s taste and meet their needs.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Integrating it with the range of AWS serverless computing, networking, and content delivery services like AWS Lambda , Amazon API Gateway , and AWS Amplify facilitates the creation of an interactive tool to generate dynamic, responsive, and adaptive logos. We recommend using the us-east-1 Obtain access to the Stability SDXL 1.0

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AWS Machine Learning Blog

NOVEMBER 27, 2023

of Large Model Inference (LMI) Deep Learning Containers (DLCs) and adds support for NVIDIA’s TensorRT-LLM Library. This file contains the required configurations for the Deep Java Library (DJL) model server to download and host the model. Qing Lan is a Software Development Engineer in AWS.

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AWS Machine Learning Blog

NOVEMBER 26, 2023

Amazon Personalize is excited to announce the new Next Best Action ( aws-next-best-action ) recipe to help you determine the best actions to suggest to your individual users that will enable you to increase brand loyalty and conversion.

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AWS Machine Learning Blog

JUNE 7, 2023

Libraries such as DeepSpeed (an open-source deep learning optimization library for PyTorch) address some of these challenges, and can help accelerate model development and training. Training setup We provisioned a managed compute cluster comprised of 16 dl1.24xlarge instances using AWS Batch.

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AWS Machine Learning Blog

OCTOBER 8, 2024

Prerequisites Before you begin, make sure you have the following requirements in place: An AWS account. A Hugging Face access token so you can download the Meta Llama 3 models and tokenizer to use later. You can download the dataset with a SageMaker processing job available in the sample Jupyter notebook.

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AWS Machine Learning Blog

JULY 8, 2024

Stable Diffusion XL by Stability AI is a high-quality text-to-image deep learning model that allows you to generate professional-looking images in various styles. AWS CodeCommit is a fully managed source control service that hosts private Git repositories. Kohya SS can be used with a GUI.

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PyImageSearch

SEPTEMBER 30, 2024

Deployment Server Tools: Kubernetes, Docker Swarm, AWS CodeDeploy Purpose: Automates the deployment of applications to staging or production environments. Do you think learning computer vision and deep learning has to be time-consuming, overwhelming, and complicated? Docker images and binary files). That’s not the case.

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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. Hugging Face is a popular open source hub for machine learning (ML) models.

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AWS Machine Learning Blog

APRIL 17, 2023

However, as the size and complexity of the deep learning models that power generative AI continue to grow, deployment can be a challenging task. Then, we highlight how Amazon SageMaker large model inference deep learning containers (LMI DLCs) can help with optimization and deployment.

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AWS Machine Learning Blog

APRIL 8, 2024

of Large Model Inference (LMI) Deep Learning Containers (DLCs). LMI-Distributed backend At AWS re:Invent 2023, LMI-Dist added new, optimized collective operations to speed up communication between GPUs, resulting in lower latency and higher throughput for models that are too big for a single GPU.

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

The compute clusters used in these scenarios are composed of more than thousands of AI accelerators such as GPUs or AWS Trainium and AWS Inferentia , custom machine learning (ML) chips designed by Amazon Web Services (AWS) to accelerate deep learning workloads in the cloud. at a minimum).

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