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Last updated : May 19, 2020
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
May 2020
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Our teams have continued to use and appreciate the PyTorch machine learning framework, and several teams prefer PyTorch over TensorFlow. PyTorch exposes the inner workings of ML that TensorFlow hides, making it easier to debug, and contains constructs that programmers are familiar with such as loops and actions. Recent releases have improved performance of PyTorch, and we've been using it successfully in production projects.

May 2018
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

PyTorch is a complete rewrite of the Torch machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. ThoughtWorks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow’s performance for production-scale training and classification.

Nov 2017
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

PyTorch is a complete rewrite of the Torch machine learning framework from Lua to Python. Although quite new and immature compared to Tensorflow, programmers find PyTorch much easier to work with. Because of its object-orientation and native Python implementation, models can be expressed more clearly and succinctly and debugged during execution. Although many of these frameworks have emerged recently, PyTorch has the backing of Facebook and broad range of partner organisations, including NVIDIA, which should ensure continuing support for CUDA architectures. Thoughtworks teams find PyTorch useful for experimenting and developing models but still rely on TensorFlow’s performance for production-scale training and classification.

Published : Nov 30, 2017
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