Battle of The Giants: TensorFlow vs PyTorch 2023
Introduction
When it comes to deep learning, there are several libraries to choose from, each with their own strengths and weaknesses. Some of the most popular libraries include TensorFlow and PyTorch. In this article, we will go through some considerations for those who want to pick just one of them.
Quick Rundown
➡️ 𝑷𝒚𝑻𝒐𝒓𝒄𝒉 𝒊𝒔 𝒕𝒉𝒆 𝒌𝒊𝒏𝒈 𝒐𝒇 𝒑𝒐𝒑𝒖𝒍𝒂𝒓𝒊𝒕𝒚
Both Pytorch and TensorFlow have risen quite a lot in popularity, probably due to transfer learning and the pre-trained models offered by 🤗 HugginFace. Though, it seems that model availability is superior for PyTorch.
Google Trends shows a clear rise in search popularity of PyTorch against TensorFlow closing completely their previous gap, while PyTorch dominates papers’ implementations with a relative score of over 92% (over the last 5 years up until 01/2023), according to PapersWithCode. Not only PyTorch has climbed the ladder of popularity, but it also seems to follow a strong trend as well that will continue.
➡️ 𝑷𝒚𝑻𝒐𝒓𝒄𝒉 𝒊𝒔 𝒘𝒂𝒚 𝒆𝒂𝒔𝒊𝒆𝒓 𝒕𝒐 𝒅𝒆𝒃𝒖𝒈 𝒕𝒉𝒂𝒏 𝑻𝒆𝒏𝒔𝒐𝒓𝑭𝒍𝒐𝒘
PyTorch offers you the option to access each line, allowing you to use a debugger and prints (dynamic computation graph, the graph is built on the fly, and operations are executed immediately), which is one of the core reasons it is preferred for research.
Tensor objects and operations in TensorFlow contain symbolic references (static computation graph, builds the graph first with placeholders and it feeds data to them), but PyTorch objects contain the actual data, making life easier for programmers. For that reason, PyTorch is easier to learn and work with even though some parts can be more hands-on than TF.
➡️ 𝑻𝒆𝒏𝒔𝒐𝒓𝑭𝒍𝒐𝒘 𝒐𝒇𝒇𝒆𝒓𝒔 𝒃𝒆𝒕𝒕𝒆𝒓 𝒅𝒆𝒑𝒍𝒐𝒚𝒎𝒆𝒏𝒕 𝒄𝒂𝒑𝒂𝒃𝒊𝒍𝒊𝒕𝒊𝒆𝒔
PyTorch has lagged behind in this area, but since 2020 it has made large steps in simplifying deployment by introducing TorchServe. TorchServe is an open-source deployment framework, that enables native deployment options that support REST and gRPC APIs.
However, it is still inferior to TensorFlow Extended’s TF Serving platform. TF’s solution is more mature for deployment and probably a more stable solution to this moment as it was designed for this purpose. The keyword here is “to this moment”. Definitely worth keeping an eye out for this.
➡️ 𝑻𝒓𝒂𝒊𝒏𝒊𝒏𝒈 𝑺𝒕𝒂𝒕𝒔 𝑽𝒊𝒔𝒖𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏 — 𝑻𝒊𝒆
Both libraries offer similar training analytics options, as PyTorch has closed the gap by integrating TensorBoard into its framework.
👉 Bottom Line
In my eyes, both libraries are equally adequate to get things done and both must be a tool under your belt.
Though if you want to pick just one to start with and you are into research, you might find yourself better off using PyTorch due to the debugging capabilities, and, if you want to deploy your model, you might find TensorFlow better optimized for that purpose.
But, even if you only have experience with PyTorch and you want to reap the benefits of TF in terms of deployment, you can, by converting Pytorch to TF or TFLite via the ONNX library. More details on this in a future post 😇. Follow to get notified.
