Setting up your training environment
Establishing a robust training environment for LLMs involves creating a setup where models can learn effectively from data and improve over time. The steps to create such an environment are discussed next.
Hardware infrastructure
For training LLMs, the hardware infrastructure is an essential foundation that ensures the training process is efficient and effective. Here’s an in-depth look at the key components:
- Graphics processing units (GPUs): GPUs are specialized hardware designed to handle parallel tasks efficiently, which makes them ideal for the matrix and vector computations required in deep learning. Modern LLMs often necessitate the use of high-end GPUs with a large number of cores and substantial onboard memory to handle the computation loads.
- Tensor processing units (TPUs): TPUs are custom chips developed specifically for ML workloads. They are optimized for the operations used in neural network training, offering...
