> For the complete documentation index, see [llms.txt](https://fall2019.fullstackdeeplearning.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/frameworks-and-distributed-training.md).

# Frameworks and Distributed Training

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Frameworks and Distributed Training - Infrastructure and Tooling
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## 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.


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