From exploration to deployment - combining PyTorch and TensorFlow for Deep Learning


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Despite the many deep learning frameworks out in the wild few haveachieved widespread adoption. Two of them are TensorFlow and PyTorch.Where PyTorch relies on a dynamic computation graph TensorFlow goes fora static graph. Where TensorFlow shows greater adoption and additionaluseful extensions with TensorFlow Serving and TensorBoard, Pytorchproves useful trough its easy and more pythonic API.Data scientists are confronted with explorative challenges, but alsoneed to be aware of model deployment and production. Do we need tosingle out frameworks until we end up with the only one or is there acase for joint usage of two deep learning frameworks? Can we leveragethe strengths of the frameworks for different tasks along the path fromexploration to production?In my talk, I want to present a case combining the benefits of PyTorchand TensorFlow using the first for explorative and latter for deploymenttasks. Therefore, I will choose a common deep learning challenge anddiscuss the strengths and weaknesses of both frameworks along a demothat brings a model from development into production.