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PyTorch Deep Learning Hands-On

You're reading from   PyTorch Deep Learning Hands-On Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781788834131
Length 250 pages
Edition 1st Edition
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Authors (2):
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Sherin Thomas Sherin Thomas
Author Profile Icon Sherin Thomas
Sherin Thomas
Sudhanshu Passi Sudhanshu Passi
Author Profile Icon Sudhanshu Passi
Sudhanshu Passi
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Toc

Exploring RedisAI

We have seen the optimization we can get through TorchScript, but what will we do with the optimized binary? Yes, we can load it in the C++ world and make a Go server, and load it there, but that's still painful.

Redis Labs and Orobix brought us another solution called RedisAI. It is a highly optimized runtime built on top of LibTorch and can accept compiled TorchScript binaries for serving through the Redis protocol. For people who don't have prior experience with Redis, http://redis.io has good documentation and the introduction doc given there [3] should be a good start.

RedisAI comes with three options to configure three backends: PyTorch, TensorFlow, and ONNX Runtime. It doesn't stop there: RedisAI uses DLPack in the backend to enable tensors to pass through different frameworks without much conversion cost.

What does that even mean? Let's say you have a TensorFlow model that converts the human face to a 128-dimensional embedding (this...

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