Now prepare data and build models using TensorFlow 2.6 and PyTorch 1.8 in Amazon SageMaker Studio Notebooks
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up SageMaker Studio Notebooks to interactively explore datasets and build ML models. The notebooks come pre-configured with deep learning environments for AWS-optimized TensorFlow and PyTorch to quickly get started with building models. Starting today you can access two new environments for TensorFlow 2.6 and PyTorch 1.8.
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). With a single click, data scientists and developers can quickly spin up SageMaker Studio Notebooks to interactively explore datasets and build ML models. The notebooks come pre-configured with deep learning environments for AWS-optimized TensorFlow and PyTorch to quickly get started with building models. Starting today you can access two new environments for TensorFlow 2.6 and PyTorch 1.8.
Data preparation is a foundational step of any data science and ML workflow. Therefore, the new TensorFlow 2.6 and PyTorch 1.8 environments come built-in with the recently introduced capability to visually browse and connect to Amazon EMR clusters right from the SageMaker Studio Notebook. Thus, you can interactively explore, visualize and prepare petabyte-scale data using Spark, Hive and Presto on Amazon EMR and build ML models using the latest deep learning frameworks without leaving the notebook.
These features are generally available in all AWS Regions where SageMaker Studio is available and there are no additional charges to use this capability. For complete information on pricing and regional availability, please refer to the SageMaker Studio pricing page. To learn more, see “Prepare Data at Scale with Studio Notebooks” in the SageMaker Studio Notebooks user guide.