PyTorch continues to be our choice of machine learning (ML) framework. Most of our teams prefer PyTorch over TensorFlow. PyTorch exposes the inner workings of ML that TensorFlow hides, making it easier to debug. With dynamic computational graphs, model optimization is much easier compared to any other ML framework. The extensive availability of State-of-the-Art (SOTA) models and the ease of implementing research papers make PyTorch stand out. When it comes to graph ML, PyTorch Geometric is a more mature ecosystem and our teams have had great experiences with it. PyTorch has also gradually bridged gaps when it comes to model deployment and scaling; our teams have used TorchServe to serve pretrained models successfully in production, for example. With many teams defaulting to PyTorch for their end-to-end deep-learning needs, we happily recommend adopting PyTorch.
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.
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.
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.
