Let's now summarize some of the advantages TF.js brings over TensorFlow, besides the ones we have already talked about in this chapter:
- Automatic GPU support: You don't need to install CUDA or GPU drivers separately with TF.js to benefit from the GPUs present on the system. This is because the browser itself implements GPU support.
- Integration: It is fairly simple to integrate TF.js into a web development project using Node.js and then import pretrained models to the project and run them in the browser.
However, it also has several disadvantages that have to be kept in mind whenever developing for production. Some of these are as follows:
- Speed: TF.js is suitable for small datasets. On large-scale datasets, the computation speed suffers heavily and is nearly 10x slower.
- Lack of a tensor board: This great tool, which enables...