🧠Introduction: What Is torch.distributed.checkpoint? In large-scale distributed training, saving and restoring model state is not as simple as calling torch.save() and torch.load(). When training across many GPUs or nodes, traditional checkpointing becomes slow, memory-intensive,
🧠Introduction: What Is torch.distributed.pipelining? As deep learning models become increasingly large, training them on a single GPU or even with traditional data parallelism becomes challenging. That’s where pipeline parallelism comes in. torch.distributed.pipelining is a
🚀 Introduction: What Is torch.distributed.optim? In distributed deep learning, syncing model weights across devices is crucial for consistent training. That’s where torch.distributed.optim comes in. torch.distributed.optim is a PyTorch module that wraps standard optimizers (like Adam,
Introduction: What Is torch.distributed.tensor.parallel? torch.distributed.tensor.parallel is a module in PyTorch that provides tools to implement tensor parallelism—a technique used to split large model tensors (e.g., weights) across multiple GPUs. Unlike data parallelism, where each GPU
What is torch.distributed.fsdp.fully_shard? The fully_shard function is PyTorch’s granular, module-level API for applying Fully Sharded Data Parallelism (FSDP) to specific model components. Unlike wrapping entire models with FSDP, fully_shard enables: Key Differences from FSDP Wrapping Feature FSDP() Wrapper fully_shard Scope Entire
What is torch.distributed.fsdp? torch.distributed.fsdp (Fully Sharded Data Parallel) is PyTorch’s advanced distributed training strategy that optimizes memory usage by sharding model parameters, gradients, and optimizer states across multiple GPUs. Unlike traditional DDP (DistributedDataParallel) that replicates the entire model,
What is torch.distributed.elastic? torch.distributed.elastic is PyTorch’s framework for fault-tolerant, elastic distributed training that automatically adapts to cluster changes. Unlike static distributed training, elastic training: Key Components Code Examples 1. Basic Elastic Training Setup import torch.distributed.elastic as elastic from
What is torch.distributed.algorithms.join? torch.distributed.algorithms.join is PyTorch’s solution for handling uneven input distributions in distributed training scenarios. This utility ensures all processes in a distributed group complete their computations properly, even when some processes have more data
What is torch.distributed.tensor? torch.distributed.tensor (also known as Distributed Tensor) is a PyTorch feature that enables efficient tensor operations across multiple GPUs or machines. It allows large tensors to be split (sharded) and processed in parallel, optimizing memory usage
What is torch.distributed? torch.distributed is PyTorch’s built-in module for distributed training, enabling parallel processing across multiple GPUs or machines. It supports different communication backends (like NCCL and Gloo) and provides tools for synchronizing gradients, data parallelism, and