Tag: PyTorch

Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now training LLMs of unprecedented size ranging from 1 billion to over 175 billion parameters. Training these LLMs requires significant compute resources and time as hundreds […]

In this post, we demonstrate one of the many options that you have to take advantage of AWS’s broadest and deepest set of AI/ML capabilities in a multicloud environment. We show how you can build and train an ML model in AWS and deploy the model in another platform. We train the model using Amazon SageMaker, store the model artifacts in Amazon Simple Storage Service (Amazon S3), and deploy and run the model in Azure.

When deploying Deep Learning models at scale, it is crucial to effectively utilize the underlying hardware to maximize performance and cost benefits. For production workloads requiring high throughput and low latency, the selection of the Amazon Elastic Compute Cloud (EC2) instance, model serving stack, and deployment architecture is very important. Inefficient architecture can lead to […]

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. In general, an operator describes […]

Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to help separate out the relevant and meaningful facts and figures. However, the same piece of news can have a positive or negative impact on stock prices, which presents a challenge for […]

In the past decade, we have seen Deep learning (DL) science adopted at a tremendous pace by AWS customers. The plentiful and jointly trained parameters of DL models have a large representational capacity that brought improvements in numerous customer use cases, including image and speech analysis, natural language processing (NLP), time series processing, and more. […]

Remember the screech of the dial-up and plain-text websites? It was in that era that the Amazon.com website launched in the summer of 1995. Like the rest of the web, Amazon.com has gone through a digital experience makeover that includes slick web controls, rich media, multi-channel support, and intelligent content placement. Nonetheless, there are certain […]

Training and inference are crucial components of a machine learning (ML) development cycle. During the training phase, you teach a model to address a specific problem. Through this process, you obtain binary model files ready for use in production. For inference, you can choose among several framework-specific solutions for model deployment, such as TensorFlow Serving […]

Amazon SageMaker and the AWS Deep Learning AMIs (DLAMI) now provide an easy way to evaluate the PyTorch 1.0 preview release. PyTorch 1.0 adds seamless research-to-production capabilities, while retaining the ease-of-use that has enabled PyTorch to rapidly gain popularity. The AWS Deep Learning AMI comes pre-built with PyTorch 1.0, Anaconda, and Python packages, with CUDA and […]

Starting today, you can easily train and deploy your PyTorch deep learning models in Amazon SageMaker. This is the fourth deep learning framework that Amazon SageMaker has added support for, in addition to TensorFlow, Apache MXNet, and Chainer.  Just like with those frameworks, now you can write your PyTorch script like you normally would and […]