Nuestros equipos han venido usando, y les sigue gustando, el framework de machine learning PyTorch, y muchos equipos prefieren PyTorch sobre TensorFlow. PyTorch expone el funcionamiento interno de ML que TensorFlow oculta, haciendo que sea más fácil depurarlo, y contiene componentes con los que la mayoría de las personas programadoras están familiarizadas, como bucles y acciones. Los lanzamientos recientes han mejorado el desempeño de PyTorch, y nosotros lo hemos estado usando con éxito en proyectos en producción.
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.
