🚀 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…
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…
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: Selective sharding of individual model components Mixed…
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)…
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: Handles node failures gracefully - Automatically recovers from worker crashes…
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…
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…
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…
With the rise of AI deployment in production environments, exporting a trained model is a crucial step in any machine learning pipeline. In PyTorch, this is where torch.export comes in—a…
PyTorch is known for its flexibility and dynamic computation graph, but what powers its performance under the hood is something called “backends.” In PyTorch, the torch.backends module plays a vital…