Pythot Lightning is on new and shining or who people uses but don't let that trick you. It's what it Pythot Lightning or PL or PL. I'm not quite sure. Pythot Lightning 2 mouthful. Pythot is a very popular Python framework for training learning model and PL is a Pythot last-way wrapper for high performance AI research. If TensorFlow has Keras at higher API, then PL is at equivalent but for Pythot. PL is last year and is getting very popular recently. So what's cool about it and why it would you not use it? Let's look at an example. This is how you train almost any deepening model in Pythot. First you define the task and then you create a model and optimizer. Then you need to make the training look. These are very fundamental and all Pythot Lightning have these same steps but people are stupid. You don't know how to structure or make that code readable. These same steps can actually look like a mess especially when dealing with more complex machine learning tasks. So PL go to help people better organize the code by following a standard structure. It also eliminates a bunch of model plates for you. These are the blue steps such as any translation, locking, looping, cleaning up. Now you need to minimize the core capacity unique to your task. If other tasks created at a set, there is a model and if you find a training step. This is a basic structure where you create a PL model with also a Pythot model and then you override some method to customize. One the model is defined, even instantiated, and to create a trainer, then you call fit. The trainer will learn the entire show for you, feeling all the missing pieces. Here is how I implement a for example with PL. Now it looks much more readable in red overnight. My mom will be very proud if she sees this. What else is cool about PL you ask? Well, after a bunch of convenient utilities make your life easier. It integrates with a bunch of popular locking mechanisms that are tensable. It can be easily configured with multiple GPU TVUs or in this dreamt setting mode for power user. You can switch to a sitting-bit position training easily, but also have a built-in compiler and a bunch of other features. If you are thinking, wow, it is so great! You just fell into the trap. PL is for high-performance AI user and researchers are terrible engineers. I'm just quoting. Now you cannot expect efficient to excel at, say, niving. It difficult to master one that alone two skills. Research is hard. Engineering is maybe harder. In fact, even engineers are also terrible at engineering. For example, look at the mess that it tends to blow up. If you are a pure researcher or a solid engineer with a good understanding of people learning my libraries. If you are a power user, where a lot of pure features are very valuable. If you are working on a big project with a team, there are a bunch of good use cases for PL. A lot of users are like students who are still learning, hobbyists, independent researchers or maybe that apply research in an industry. You run a good engineering. You need to structure your code well and you need to be able to implement all the stuff that PL does for you. Using PL can make you lazy and rusty. So I would recommend using PL only as a reference until you become a solid engineer. Leading into high-level APIs and characters, and hiding away the details of network and optimization designs isn't a good approach for research. And to get credit, the PL authors is aware and tries to avoid that, but PL committed an act that is so immoral, so blasphemous that I have to condemn it right here and now. It traps my training loop. That's right. It's the same shit that we have seen countless times before. You need to understand, a cell with a back cook will kill the chickens themselves. A samurai cleansed his sauce himself. Training loop is a sacred place, and this thing just composes all over it. No training loop is the same, every one of them is unique. In conclusion, if you prefer using a heart watch, let me. Then feel free to use it whatever you cast. I'm not your mom.