Amazon SageMaker Studio now supports multi-GPU instances
Today we are excited to announce that Amazon SageMaker Studio now supports multi-GPU instances of ml.g4dn.12xlarge, ml.p3.8xlarge, and ml.p3.16xlarge. Multi-GPU instances accelerate machine learning model training significantly, allowing users to train more advanced machine learning models that are too large to fit into a single GPU. They also provide the flexibility to process larger batches of data such as 4k images for image classification and object detection.
Today we are excited to announce that Amazon SageMaker Studio now supports multi-GPU instances of ml.g4dn.12xlarge, ml.p3.8xlarge, and ml.p3.16xlarge. Multi-GPU instances accelerate machine learning model training significantly, allowing users to train more advanced machine learning models that are too large to fit into a single GPU. They also provide the flexibility to process larger batches of data such as 4k images for image classification and object detection.
Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps, giving you complete access, control, and visibility required to build, train, and deploy models. Within the unified SageMaker Studio visual interface, you can perform all ML development activities including notebooks, experiment management, automatic model creation, debugging, and model drift detection. For more information, please visit the documentation.