Write and deploy machine learning models easily in Nodejs using Tensorflow.js.
Machine Learning in Node.js using Tensorflow.js
AI Generated Video Summary
1. Introduction to TensorFlow.js in Node.js
2. Utilizing TensorFlow.js in Node.js
TensorFlow.js provides better performance than Python versions in certain cases, such as BERT classification. TensorFlow.js in Node.js can be utilized through three different packages: TensorFlow CPU, TensorFlow GPU, and vanilla package. The CPU package accelerates mathematical computations, while the GPU package runs tensor operations on the GPU for even better performance. The vanilla package, which does not rely on TensorFlow, can be used on other devices that support Node.js. Node.js bindings for TensorFlow.js should be set up to avoid blocking the main thread. APIs, such as dfNode and TensorBoard, are available once the package is imported. Feel free to connect with me on social platforms for any queries regarding TensorFlow.js.
So that means that TensorFlow.js itself is quite optimized for running very industrial standard models as well, and also for newer models at the same time. And in some cases, the TensorFlow.js model actually provides a lot more better performance as compared to the Python versions. For example, BERT, which is a state-of-the-art language model for natural language processing, we can see that over here, there is actually a two times better performance boost by actually using Node.js as compared to a Python-based model that is running this BERT classification.
Now, coming on to the most important part, that is, you know, how can we start utilizing the TensorFlow.js in Node.js. So we get namely three different packages that you can install, like the npm packages. So the first one is the TensorFlow CPU. The TensorFlow CPU is, you know, whenever we are importing this package, the module that we get is basically accelerated by the TensorFlow C binary and it runs on the CPU. And the TensorFlow on the CPU uses the hardware acceleration to, let's say, accelerate any kind of mathematical computations, for example, linear algebra.
Now, one of the important considerations that need to be made is that the Node.js bindings run on the backend for TensorFlow.js that implements them synchronously. That means that whenever we are running the Node.js bindings on a production application like a web server, we should actually set up a drop queue or a server or like worker threads so that it does not block your main thread. Now, there are also support for APIs because once we have imported the package, all of the normal TensorFlow.js symbols that we use can be appear once we have imported the specific module. Like, for example, one of them is like the dfNode that contains Node.js specific APIs and TensorBoard is actually a notable example of the Node.js specific APIs.
Now, this is a sample code that should give you an example where we have defined like a model and we are training it by utilizing the tf.node.tensorboard. That helps in your model training. With that, that finishes off my presentation and I hope that you have liked it and you can connect with me on these social platforms and to ask any questions regarding TensorFlow.js. Again, do follow any kind of TensorFlow.js models using the hashtag madewithTensorFlow.js on Twitter and on LinkedIn. And again, feel free to connect with me for any queries regarding TensorFlow.js. I hope you have liked it.