# Frameworks and Distributed Training {% embed url="" %} Frameworks and Distributed Training - Infrastructure and Tooling {% endembed %} ## Summary * 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. * [**fast.ai**](http://fast.ai) is a solid option for beginners who want to iterate quickly. * 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. * [**Ray**](http://docs.ray.io/) is an open-source project for effortless, stateful, parallel, and distributed computing in Python. * [**RaySGD**](https://docs.ray.io/en/latest/raysgd/raysgd_pytorch.html) is a library for distributed data parallel training that provides fault tolerance and seamless parallelization, built on top of [**Ray**](http://docs.ray.io/). * **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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/frameworks-and-distributed-training.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.