Distributed Training Foundations (Part I) | Tensor, Pipeline, Data Parallism

1. 1Identify the limitations of single-device training using Tensor
2. 2Configure data parallelism to split data across multiple devices
3. 3Build a pipeline to manage distributed training workflows
4. 4Apply pipeline parallelism to parallelize model training
5. 5Test the performance of distributed training using a sample dataset

Data scientists and machine learning engineers can benefit from understanding distributed training to improve model performance and reduce training time

💡 Distributed training is necessary to overcome the scaling wall and train large-scale machine learning models efficiently

Scale your ML models with distributed training!