# HyperPod checkpointless training features
<a name="sagemaker-eks-checkpointless-features"></a>

See the following pages to learn about the training features in checkpointless training.

**Topics**
+ [Amazon SageMaker HyperPod checkpointless training repositories](#sagemaker-eks-checkpointless-repositories)
+ [Collective communication initialization improvements](sagemaker-eks-checkpointless-features-communication.md)
+ [Memory mapped dataloader](sagemaker-eks-checkpointless-features-mmap.md)
+ [In-process recovery and checkpointless training](sagemaker-eks-checkpointless-in-process-recovery.md)

## Amazon SageMaker HyperPod checkpointless training repositories
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[ HyperPod checkpointless training](https://github.com/aws/sagemaker-hyperpod-checkpointless-training#) accelerates recovery from cluster faults in large-scale distributed training environments through framework-level optimizations. These optimizations are delivered via a base container image that includes enhanced NCCL initialization improvements, data loading optimizations, and in-process and checkpointless recovery components. The HyperPod checkpointless training package is built on this foundation.

Checkpointless training is enabled via three optimization tracks that run in concert:
+ **Communication initilization improvements (NCCL and Gloo)** - Eliminate communication bottlenecks by decentralizing rank peer and ring information (red box below).
+ **Data loading optimizations** - Reduce the time required to serve the first batch of data during restart operations (orange boxes below).
+ **Program restart overhead reduction** - Minimize restart costs and enable checkpointless replenishment through process recovery on healthy nodes (blue and green boxes below).

![\[alt text not found\]](http://docs.amazonaws.cn/en_us/sagemaker/latest/dg/images/hyperpod/hyperpod-checkpointless-optimization-tracks.png)