Distributed training of deep learning models has become increasingly essential due to the growing size of datasets and the complexity of models. This training process is typically represented as dataflow graphs, where nodes are computational operators and edges are multi-dimensional tensors. A single training iteration involves a forward pass of data, loss computation, and a backward pass to update model weights, repeated until the model's loss reaches a global minimum. As AI progresses, larger models are being developed, which, while improving performance, also increase computational costs significantly. For instance, training models like GPT-3, which has 175 billion parameters, would take an impractical 355 years on a single GPU. This highlights the necessity for distributed training, which enhances developer productivity, shortens time to market, and improves cost efficiency. Two primary types of parallelism can be utilized in distributed training: data parallelism, which involves splitting data while keeping the model intact, and model parallelism, which divides the model itself across multiple devices. This discussion focuses on pipeline parallelism, a method that efficiently trains large models. Effective distributed communication is crucial for optimizing training processes. Various collective communication schemes exist, such as scatter, gather, reduce, and AllReduce, which allows for aggregation without a central server. The AllReduce algorithm, while sequential, can be improved through methods like AllReduce-Ring and AllReduce-Recursive Halving, which enhance time and bandwidth efficiency. Model parallelism remains an active research area, with notable frameworks like GPipe and Alpa making strides in this domain. GPipe simplifies model parallelism by partitioning networks into balanced cells, allowing for efficient scaling across devices. It processes mini-batches as micro-batches, optimizing pipeline synchronization and minimizing communication overhead. Alpa, on the other hand, automates inter- and intra-operator parallelism, organizing parallelization techniques hierarchically to match the structure of compute clusters. This approach enhances device utilization and reduces communication costs by strategically mapping parallelism to the cluster's communication bandwidth. In summary, the exploration of distributed training of deep learning models reveals the importance of efficient communication strategies and parallelism techniques. The next part of this series will delve deeper into communication strategies, gradient compression, and additional methods to enhance the efficiency of distributed training.