Multinode PyTorch Model Training using MPI and Singularity

Why multiple nodes?

Multinode training in PyTorch allows for the distribution of the computational workload across multiple nodes, which results in faster model training and increased scalability. By leveraging multiple nodes, each with its own set of resources, the data can be partitioned, and computations performed in parallel, leading to improved performance. Additionally, multinode training enables the training of larger models with more data, which may be impractical or impossible to train on a single node.

Why MPI?

MPI (Message Passing Interface) is a reliable, efficient, and widely adopted standard for parallel processing that enables communication between multiple nodes in a distributed system. MPI is a good choice for multinode PyTorch model training because it provides a standardized way for nodes to communicate and synchronize their work, critical for ensuring model accuracy and consistency.

MPI can handle both synchronous and asynchronous communication, allowing for efficient data transfer and synchronization between nodes. MPI also provides fault tolerance features, essential when working with distributed systems, ensuring the training can continue even if one or more nodes fail.

Why Singularity (containers)?

Singularity is a containerization tool that enables users to run applications in a self-contained environment. It provides a consistent and reproducible environment across all nodes. This eliminates the need for manual installation and configuration of software on each node, reducing the risk of version incompatibilities and errors.

Singularity also provides security benefits, as the containerized environment is isolated from the host system. This ensures that any potential security vulnerabilities or conflicts with other software on the host system do not affect the training process.

PyTorch DDP

DistributedDataParallel (DDP) is a PyTorch module used for parallelizing the training of deep learning models across multiple nodes. It is designed to work with PyTorch's native support for distributed computing using the MPI. It enables users to parallelize their training code with minimal changes to their existing PyTorch code.

DDP divides the model and data across multiple nodes and performs the forward and backward passes in parallel, enabling faster training times. It also synchronizes the gradients across all nodes, ensuring consistency in the model parameters.