Training a model on multiple GPUs using data parallelism involves distributing data across various GPUs to enhance computational efficiency and speed. The process begins with defining a model configuration, such as the Llama model, which includes hyperparameters like vocabulary size, sequence length, and number of layers. The model utilizes components like rotary position encoding and grouped-query attention to process input data. A distributed data parallel (DDP) setup is employed to manage multiple GPUs, ensuring each GPU processes a portion of the data. The training loop involves loading data, creating attention masks, computing loss, and updating model weights using optimizers and learning rate schedulers. This approach significantly boosts training performance and is essential for handling large-scale datasets and complex models in machine learning. This matters because it enables efficient training of large models, which is crucial for advancements in AI and machine learning applications.
