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Ray train allows you to scale model training code from a single machine to a cluster of machines in the cloud, and abstracts away the complexities of distributed computing. The checkpoint is a lightweight interface provided by ray train that represents a directory that exists on local or remote storage. At its core, ray train is a tool to make distributed machine learning simple and powerful

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Ray train is a robust and flexible framework that simplifies distributed training by abstracting the complexities of parallelism, gradient synchronization, and data distribution. Ray train checkpointing can be used to upload model shards from multiple workers in parallel Ray train provides distributed data parallel training capabilities

When launching a distributed training job, each worker executes this training function

Ray train documentation uses the following conventions Train_func is passed into the trainer’s train_loop_per_worker parameter. To support proper checkpointing of distributed models, ray train can now be configured to save different partitions of the model held by each worker and upload its respective partitions directly to cloud storage. Compare a pytorch training script with and without ray train

First, update your training code to support distributed training Begin by wrapping your code in a training function # your model training code here. Each distributed training worker executes this function.