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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
试验 ? 值得一试。了解为何要构建这一能力是很重要的。企业应当在风险可控的前提下在项目中尝试应用此项技术。

我们的团队一直在使用并且很认可 PyTorch 机器学习框架,并且有几支团队对 PyTorch 的喜爱甚于 TensorFlow。PyTorch 暴露了 TensorFlow 隐藏的 ML 内部工作原理,使其更易于调试,并包含了程序员熟悉的结构,例如循环和动作。PyTorch 最新版本提高了性能,我们已在生产项目中成功使用了它。

May 2018
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

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
评估 ? 在了解它将对你的企业产生什么影响的前提下值得探索

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.

已发布 : Nov 30, 2017
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