Posted On: Aug 3, 2021

Today, we are excited to announce that Amazon SageMaker now supports Amazon EC2 M5d, R5, P3dn, and G4dn instances for SageMaker Notebook Instance. Customers are able to launch SageMaker Notebook Instance with these instance types in the regions where they are available.

Amazon EC2 R5 instances are the memory optimized instances. They are well suited for memory intensive applications such as high-performance databases, distributed web scale in-memory caches, mid-size in-memory databases, real time big data analytics, and other enterprise applications.

Amazon EC2 M5d instances deliver M5 instances backed by NVMe-based SSD block level instance storage physically connected to the host server. M5d instances are ideal for workloads that require a balance of compute and memory resources along with high-speed, low latency local block storage including data logging and media processing.

Amazon EC2 P3dn instances are optimized for distributed machine learning and HPC applications. Their faster networking, new processors with additional vCPUs, doubling of GPU memory, and fast local instance storage enable developers to not only optimize performance on a single instance but also significantly lower the time to train their ML models or run more HPC simulations by scaling out their jobs across several instances.

Amazon EC2 G4dn instances are powered by NVIDIA T4 GPUs. They are the lowest cost GPU-based instances in the cloud for machine learning inference and small-scale training. They also provide high performance and are a cost-effective solution for graphics applications that are optimized for NVIDIA GPUs using NVIDIA libraries such as CUDA, CuDNN, and NVENC. They provide up to 8 NVIDIA T4 GPUs, 96 vCPUs, 100 Gbps networking, and 1.8 TB local NVMe-based SSD storage and are also available as bare metal instances.

Amazon SageMaker Notebook Instance is the machine learning (ML) compute instance running the Jupyter Notebook App. SageMaker manages creating the instance and related resources. Use Jupyter notebooks in your notebook instance to prepare and process data, write code to train models, deploy models to SageMaker hosting, and test or validate your models.  For more information, visit the Amazon SageMaker Notebook Instance documentation for details.