Here’s the translation into American English:
Amazon has introduced a new command-line interface (CLI) and a software development kit (SDK) for its Amazon SageMaker HyperPod service. These resources are designed to simplify the use of advanced capabilities in distributed training and inference of artificial intelligence models. With these tools, data scientists and machine learning experts will be able to manage large-scale models in a more accessible and efficient manner.
The SageMaker HyperPod CLI provides users with an intuitive experience, allowing them to interact with distributed systems without having to deal with their inherent complexity. Through simple commands, professionals can initiate training jobs, fine-tune models, deploy inference endpoints, and monitor cluster performance. These features make it an ideal option for those looking to conduct rapid and iterative experiments.
On the other hand, the SDK is intended for use cases that require more detailed control. It provides programmatic access that simplifies the customization of machine learning workflows. By using a Python interface, developers can precisely define the parameters needed for training and deploying models.
In a recent demonstration, practical examples were provided on how to use the CLI and SDK to train and deploy large language models on SageMaker HyperPod, utilizing techniques such as Fully Sharded Data Parallel training.
To start using these new tools, users must meet certain prerequisites, such as installing specific Kubernetes operators in their cluster. The implementation of the CLI and SDK is straightforward and can be achieved through pip commands, ensuring that they have the latest version to access all new features.
With the CLI, data scientists can set up cluster contexts and launch machine learning jobs without requiring deep knowledge of infrastructure. For those seeking a more programmatic experience, the SageMaker HyperPod SDK offers greater flexibility and customization options.
These innovations will facilitate the process of taking artificial intelligence models from the experimentation phase to production, enabling organizations to deploy AI solutions more quickly and with less complexity.
via: MiMub in Spanish
