Frameworks and Distributed Training
How to choose a deep learning framework? How to enable distributed training for your models?
Frameworks and Distributed Training - Infrastructure and Tooling
- Unless you have a good reason not to, you should use either TensorFlow or PyTorch.
- Both frameworks are converging to a point where they are good for research and production.
- Distributed training of neural networks can be approached in 2 ways: (1) data parallelism and (2) model parallelism.
- Practically, data parallelism is more popular and frequently employed in large organizations for executing production-level deep learning algorithms.
- Model parallelism, on the other hand, is only necessary when a model does not fit on a single GPU.
- Horovod is Uber’s open-source distributed deep learning framework that uses a standard multi-process communication framework, so it can be an easier experience for multi-node training.