AWS Machine Learning Blog

OCTOBER 2, 2023

The sample use case used for this series is a visual quality inspection solution that can detect defects on metal tags, which you can deploy as part of a manufacturing process. Prepare Edge devices often come with limited compute and memory compared to a cloud environment where powerful CPUs and GPUs can run ML models easily.

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AssemblyAI

JANUARY 19, 2023

Stable Diffusion allows you to create incredible images like the one below with only a sentence; but it requires a GPU to run in a reasonable amount of time. Since GPUs are expensive and in short supply, many users opt to instead pay for credits in a web app like DreamStudio in order to use Stable Diffusion in the cloud.

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AWS Machine Learning Blog

APRIL 19, 2024

Initially, our model fine-tuning took hours of CPU time, so a framework for scaling model fine-tuning on GPUs was imperative. Our deep learning models have non-trivial requirements: they are gigabytes in size, are numerous and heterogeneous, and require GPUs for fast inference and fine-tuning. Karpenter adds g4dn.metal and g4dn.12xlarge

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AWS Machine Learning Blog

NOVEMBER 20, 2023

KT’s AI Food Tag is an AI-based dietary management solution that identifies the type and nutritional content of food in photos using a computer vision model. The AI Food Tag can help patients with chronic diseases such as diabetes manage their diets. In this post, we describe KT’s model development journey and success using SageMaker.

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AWS Machine Learning Blog

MAY 25, 2023

We need access to accelerated instances (GPUs) for hosting the LLMs. Prerequisites To implement the solution provided in this post, you should have an AWS account and familiarity with LLMs, OpenSearch Service and SageMaker.

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Dataconomy

SEPTEMBER 6, 2023

ADLINK Pocket AI is a portable GPU that’s turning heads in the world of external GPUs (eGPUs). This pint-sized powerhouse, no larger than a deck of playing cards, packs a punch with its Thunderbolt 3 connectivity and the mighty NVIDIA RTX A500 4GB GDDR6 GPU under the hood.

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Flipboard

OCTOBER 9, 2023

Additionally, the -t (or --tag ) flag is used to give a nametag to your image. Using the -t flag allows you to tag your build with a name that can be used to reference it later. Using the --gpus flag allows you to pass which GPUs on the local machine should be available to the container. Example: docker build. -t

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IBM Data Science in Practice

MARCH 21, 2023

WMLA also utilizes both CPUs and GPUs that are dynamically allocated. WMLA provides large model support that helps increase the amount of memory available for deep learning models (up to 16 GB or 32 GB per network layer), enabling more complex models and data inputs. We created a deployment in WMLA for each image resolution.

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Explosion

AUGUST 1, 2019

The doc.tensor attribute gives you one row per spaCy token, which is useful if you’re working on token-level tasks such as part-of-speech tagging or spelling correction. Multiple GPUs are also not currently supported. This variable gives you a tensor with one row per wordpiece token. We are working on both of these issues.

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Flipboard

APRIL 17, 2023

And don’t forget to share your stunning convergence plots on Twitter, tagging us for a chance to win a surprise! To overcome this problem, we use GPUs. The problem is these GPUs are expensive and become outdated quickly. GPUs are great because they take your Neural Network and train it quickly. What's next?

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The MLOps Blog

JUNE 27, 2023

You can use it to speed up the inference of deep learning models on NVIDIA GPUs. TensorRT can optimize deep learning models for inference on NVIDIA GPUs, which can lead to significant performance improvements. optimizes and orchestrates GPU compute resources for AI and deep learning workloads. Reduce the size of their models.

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AWS Machine Learning Blog

APRIL 17, 2024

Despite the availability of advanced distributed training libraries, it’s common for training and inference jobs to need hundreds of accelerators (GPUs or purpose-built ML chips such as AWS Trainium and AWS Inferentia ), and therefore tens or hundreds of instances. or later NPM version 10.0.0

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Iguazio

JANUARY 22, 2024

performs transformations, cleans, arranges, versions, tags, labels, indexes, etc. To productize a GenAI application, four architectural elements are needed: 1. The data pipeline - Takes the data from different sources (document, databases, online, data warehouses, etc.),

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AWS Machine Learning Blog

APRIL 4, 2024

The following figure shows an example of our tagging system. Improved infrastructure – With SageMaker, we upgraded our existing infrastructure, and we are now using newer AWS instances with newer GPUs such as g5.xlarge. For example, we identify if the brand is on a banner or a shirt.

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Hacker News

FEBRUARY 14, 2023

Thus, for example, one might want images tagged by what’s in them, or some other attribute. And maybe one will have to explicitly go through—usually with great effort—and do the tagging. And so, for example, one might use alt tags that have been provided for images on the web. Almost certainly, I think.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

On the other hand, tuning of these base FMs for downstream tasks—which only require a few tens or hundreds of labeled data samples and inference serving—can be accomplished with only a few GPUs at the enterprise edge.

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AWS Machine Learning Blog

MAY 30, 2023

You can also apply additional filters such as account number, Amazon Elastic Compute Cloud (Amazon EC2) instance type, cost allocation tag, Region, and more. You can also include cost-allocation tags in your query for an additional level of granularity. You can build custom queries to look up AWS CUR data using standard SQL.

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The MLOps Blog

OCTOBER 3, 2023

The layer above that is where you have the providers or, for a lot of folks – if you’re a solo data scientist, for example –maybe you just need access to GPUs for machine learning models. They just need to kind of tag things like “Hey, by the way, we’re using these data sources. We’re creating these features.

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Snorkel AI

APRIL 11, 2023

And then on Amazon reviews, with several million reviews, we actually take a very small, shallow model, fastText–which can be trained in a matter of minutes on the laptop–to select data for a much larger model, VDCNN29, which takes close to 16 hours to train on GPUs. So where might we have these large label datasets?

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Snorkel AI

APRIL 11, 2023

And then on Amazon reviews, with several million reviews, we actually take a very small, shallow model, fastText–which can be trained in a matter of minutes on the laptop–to select data for a much larger model, VDCNN29, which takes close to 16 hours to train on GPUs. So where might we have these large label datasets?

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Snorkel AI

APRIL 11, 2023

And then on Amazon reviews, with several million reviews, we actually take a very small, shallow model, fastText–which can be trained in a matter of minutes on the laptop–to select data for a much larger model, VDCNN29, which takes close to 16 hours to train on GPUs. So where might we have these large label datasets?

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Dataconomy

AUGUST 23, 2023

Download and tag : Easily download the artwork to your device or add relevant tags (hashtags) to categorize or describe it. Copy-paste features : Whether you wish to retain the image prompt for future use or grab the direct URL of the generated image, Mage Space has got you covered.

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AWS Machine Learning Blog

NOVEMBER 1, 2023

f Dockerfile -t ${container_name} docker tag ${container_name} ${full_name} docker push ${full_name} LLM inference with TGI The VLP solution in this post employs the LLM in tandem with LangChain, harnessing the chain-of-thought (CoT) approach for more accurate intent classification.

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AWS Machine Learning Blog

JANUARY 9, 2023

To meet the latency and throughput goals of ML applications, GPU instances are preferred over CPU instances (given the computational power GPUs offer). With MME support for GPU, you can deploy thousands of deep learning models behind one SageMaker endpoint.

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