
Distributed Training View: This helps you understand how much time and memory is consumed in your distributed training job. Many issues occur when you take a training model and split the load into worker nodes to be run in parallel as it can be a black box. The overall model goal is to speed up model training. This distributed training view will help you diagnose and debug issues within individual nodes. 
Memory View: This view allows you to understand your memory usage better. This tool will help you avoid the famously pesky Out of Memory error by showing active memory allocations at various points of your program run. 
GPU Utilization Visualization: This tool helps you make sure that your GPU is being fully utilized.  
Cloud Storage Support: Tensorboard plugin can now read profiling data from Azure Blob Storage, Amazon S3, and Google Cloud Platform.
