I am starting a new project in Computer Vision and I'm torn between PyTorch and TensorFlow. I've heard PyTorch is better for research, but TensorFlow is better for production deployment with TFX. Is this still the case, or has the gap closed between these two major frameworks?
3 answers
The gap has closed significantly, but the "vibe" remains different. PyTorch's "Pythonic" nature and dynamic computational graph make it much easier to debug and iterate on, which is why it has almost 80% of the research market now. However, TensorFlow’s ecosystem for deployment (TF Serving, TF Lite, and TFX) is still very robust for massive enterprise pipelines. That said, PyTorch 2.0 and TorchServe have made huge strides in production readiness. If you want flexibility and community support for the latest models (like on Hugging Face), PyTorch is the clear winner today.
What about ONNX? Can't we just train in PyTorch and export to ONNX to get the best of both worlds—research flexibility and high-performance deployment?
PyTorch has definitely won the developer's heart. Most of the new papers you see on ArXiv come with a PyTorch implementation, which makes it easier to stay on the cutting edge.
Exactly, Karen. If you want to use the latest and greatest from the community, PyTorch is practically the "default" language of Deep Learning at this point.
Absolutely, Scott. That is exactly what my team does. We do all our experimentation in PyTorch because it's so much faster to prototype custom loss functions. Once we are happy with the model, we export it to an ONNX format and run it on specialized inference engines like TensorRT for production. This avoids the headaches of TensorFlow's more rigid API while still giving us top-tier execution speed on our production servers.