AWS Machine Learning Blog

APRIL 25, 2024

The added benefit of asynchronous inference is the cost savings by auto scaling the instance count to zero when there are no requests to process. SageMaker features and capabilities help developers and data scientists get started with natural language processing (NLP) on AWS with ease.

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AWS Machine Learning Blog

DECEMBER 13, 2023

We use the AWS Neuron software development kit (SDK) to access the AWS Inferentia2 device and benefit from its high performance. engine = Python # default handler for model serving option.entryPoint = djl_python.transformers_neuronx # The Hugging Face ID of a model or the s3 url of the model artifacts.

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AWS Machine Learning Blog

MAY 9, 2023

In addition, you can now use Application Auto Scaling with provisioned concurrency to address inference traffic dynamically based on target metrics or a schedule. In this post, we discuss what provisioned concurrency and Application Auto Scaling are, how to use them, and some best practices and guidance for your inference workloads.

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AWS Machine Learning Blog

JUNE 23, 2023

Use a Python notebook to invoke the launched real-time inference endpoint. Basic knowledge of Python, Jupyter notebooks, and ML. Another option is to download complete data for your ML model training use cases using SageMaker Data Wrangler processing jobs. We can monitor the export progress while we wait for it to complete.

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AWS Machine Learning Blog

MAY 31, 2023

Complete the following steps: Launch the provided CloudFormation template. When the stack is complete, you can move to the next step. Complete the following steps: On the Amazon ECR console, create a new repository. To do a complete cleanup, delete the CloudFormation stack to remove all resources created by this template.

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AWS Machine Learning Blog

OCTOBER 11, 2023

The decode phase includes the following: Completion – After the prefill phase, you have a partially generated text that may be incomplete or cut off at some point. The decode phase is responsible for completing the text to make it coherent and grammatically correct. The default is 32.

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AWS Machine Learning Blog

JUNE 13, 2023

LMI DLCs are a complete end-to-end solution for hosting LLMs like Falcon-40B. You can monitor the status of the endpoint by calling DescribeEndpoint , which will tell you when everything is complete. He has more than 15 years working with technology, from software development, infrastructure, serverless, to machine learning.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. The SageMaker Python SDK is a high-level Python API that abstracts some of the steps and configuration, and makes it easier to deploy models. For that, we are offering improvements in the Python SDK.

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AWS Machine Learning Blog

MAY 4, 2023

For ultra-large models that don’t fit into a single accelerator, data flows directly between accelerators with NeuronLink, bypassing the CPU completely. You can use the SageMaker Python SDK to deploy models using popular deep learning frameworks such as PyTorch, as shown in the following code.

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AWS Machine Learning Blog

AUGUST 4, 2023

With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. Make sure to disable sampling when importing the data.

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Unite.AI

APRIL 18, 2024

From self-driving cars to language models that can engage in human-like conversations, AI is rapidly transforming various industries, and software development is no exception. This remarkable tool leverages state-of-the-art language models like GPT-4, streamlining the development cycle and enhancing developer productivity.

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AWS Machine Learning Blog

APRIL 17, 2023

SageMaker LMI containers includes model download optimization by using the s5cmd library to speed up the model download time and container startup times, and eventually speed up auto scaling on SageMaker. It has faster loading and uses multi-process loading on Python. Rohith Nallamaddi is a Software Development Engineer at AWS.

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AWS Machine Learning Blog

JANUARY 17, 2024

Llama 2 is an auto-regressive generative text language model that uses an optimized transformer architecture. Trainium and AWS Inferentia, enabled by the AWS Neuron software development kit (SDK), offer a high-performance, and cost effective option for training and inference of Llama 2 models.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity. For MMEs, each model.py

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AWS Machine Learning Blog

JUNE 5, 2023

You can find our complete example notebook here. Then, we import the SageMaker Python SDK and instantiate a sagemaker_session to find the current region and execution role. Qing Lan is a Software Development Engineer in AWS. Xin Yang is a Software Development Engineer at AWS.

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Flipboard

APRIL 13, 2023

For instance, a financial firm that needs to auto-generate a daily activity report for internal circulation using all the relevant transactions can customize the model with proprietary data, which will include past reports, so that the FM learns how these reports should read and what data was used to generate them.

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