**Introduction** \|\| `What is DDP `__ \|\| `Single-Node Multi-GPU Training `__ \|\| `Fault Tolerance `__ \|\| `Multi-Node training <../intermediate/ddp_series_multinode.html>`__ \|\| `minGPT Training <../intermediate/ddp_series_minGPT.html>`__ Distributed Data Parallel in PyTorch - Video Tutorials ====================================================== Authors: `Suraj Subramanian `__ Follow along with the video below or on `youtube `__. .. raw:: html
This series of video tutorials walks you through distributed training in PyTorch via DDP. The series starts with a simple non-distributed training job, and ends with deploying a training job across several machines in a cluster. Along the way, you will also learn about `torchrun `__ for fault-tolerant distributed training. The tutorial assumes a basic familiarity with model training in PyTorch. Running the code ---------------- You will need multiple CUDA GPUs to run the tutorial code. Typically, this can be done on a cloud instance with multiple GPUs (the tutorials use an Amazon EC2 P3 instance with 4 GPUs). The tutorial code is hosted in this `github repo `__. Clone the repository and follow along! Tutorial sections ----------------- 0. Introduction (this page) 1. `What is DDP? `__ Gently introduces what DDP is doing under the hood 2. `Single-Node Multi-GPU Training `__ Training models using multiple GPUs on a single machine 3. `Fault-tolerant distributed training `__ Making your distributed training job robust with torchrun 4. `Multi-Node training <../intermediate/ddp_series_multinode.html>`__ Training models using multiple GPUs on multiple machines 5. `Training a GPT model with DDP <../intermediate/ddp_series_minGPT.html>`__ “Real-world” example of training a `minGPT `__ model with DDP