FastAI is a deep learning library that equips practitioners with high-level components that can quickly and efficiently deliver state-of-the-art results in standard deep learning domains and equips researchers with low-level components that can be blended and matched to build new approaches. It aims to do both ease of use, flexibility, or performance without substantial compromises. This is attainable thanks to a carefully layered architecture, which expresses common underlying patterns in decoupled abstractions of many deep learning and data processing techniques.
It has also been a significant protagonist in the development of transfer learning for natural language processing; conducted an investigation and evaluation of the research into the use of face masks for suppressing the spread of COVID-19; been a voice at the forefront of applied data ethics. Boost your skills and take up an AI ML certification.
Learning of the Blog
- What is FastAI?
- Controlling Resources
- What speeds up FastAI training?
- Benefits over other libraries
Let’s see more about what FastAI is and how it’s changing the world.
What Is FastAI?
It is a research lab started by two people: a former Kaggle President and the other a well-known AI expert. Its mission is to make AI accessible to everyone. Fast.ai can be described as a course-bundled research lab, an easy-to-use Python library with a vast community. Their library wraps popular libraries for common workflows of deep learning and machine learning and provides a user-friendly interface.
More importantly, the “top-down” approach follows.
“Top-down” is exactly like the way a sport is learned. We start by trying to play it without having to worry about rules. Once we are confident, we learn one by one of the rules and the tricks.
Similarly, fast.ai lets us create a model using just a few lines of code. After that, we can further improve it.
FastAI is designed to simplify neural network training. It is based on research into the best practices in deep learning. Has support outside the box for:
- Tabular data
- Collaborative filtering
FastAI is created on top of Pytorch and hence includes some pre-trained models such as resent18, resnet34, resnet50, resnet101, resnet152 (each with different layers) as densenet121, densenet169. It is complementary to Pytorch, a Python-based deep-learning library used in computer vision and neural network models.
You would need to use an Nvidia GPU. Unless you own a gaming PC, you’re unlikely to have an Nvidia GPU. Even if you’ve got an Nvidia GPU, you may need to download any data to train models from the internet. The best option is to use a GPU in the Cloud.
Here are a few options:
Crestle is the easiest starter option. It costs about 0.60 USD an hour. You don’t have to set up anything, only start learning.
Paperspace has an array of GPU options to choose from. The starting option is cheaper and more reliable than Crestle. The downside is you’d have to spend around an hour doing the initial setup. If you don’t have some Linux experience, it could be not easy.
AWS AND GCE
Both of these cloud providers have a wide range of GPU options, and in the long run, they might be more cost-effective than Paperspace. You could also use free credits to sign up—Google for “FastAI AWS” or “FastAI GCE.”
You can get a GPU for free using Google CoLab. Running fast.ai on CoLab is challenging, as it is mainly built for Tensorflow (Google’s deep learning toolkit). If you have time to experiment with that, you might try to make it work for fast.ai.
What Speeds Up FastAI Training?
It includes an OO class, encapsulated preprocessing, augmentation, test, training, and validation sets, multi-class versus single classification versus regression, as mentioned in the documentation in FastAI. Along with architecture model choice. Thus FastAI can determine the best architecture, preprocessing, and training parameters for that model, for that data, mostly automatically. And finally, it became more productive and made far fewer mistakes because it automated everything that could be automated. For example, it tends to customize models more difficult for Keras, especially during training. More importantly, the static computing graph on the back-end, together with Keras’ need for an extra compile() phase, means it’s hard to customize a model’s behavior once it’s built and FastAI is much quicker in this case.
What Benefits Does the FastAI Library Have Over Other Libraries?
Because of fewer codes written by AI developers, everything is much more comfortable with FastAI. As the documentation says, at the same time, FastAI delivers flexibility, speed, and ease of use. It offers many features and functionality, which makes developers customize the high-level API without engaging in low-level API parts. One example of this customization is DataBlock, which allows you to load the data in detail. As FastAI explains, DataLoader class loads both the training and the validation data classes. Besides, the process of using validation data sets while training the data would make the job easier. Beginners working with this library, therefore, use available functions and start customizing models. The figure shows four fields of applications, including vision, text, tabular, and collaborative filtering, each of which is used for different purposes.
The FastAI library also implements the learning rate finder that provides the best value for the learning rate parameter after a sample training session.
FastAI has become a popular tool for data scientists. FastAI simplifies painful aspects of model training, such as preprocessing and loading data down to a few code lines. GCP, AWS, and Azure all have already included FastAI in their machine images. The creators of FastAI, acknowledging Python’s speed and safety limitations, have announced embracing Swift as an alternative language for deep learning. Interested in learning AI and its magic? Become a Certified Artificial Intelligence Expert or an Artificial Intelligence Developer. There are many AI certificate programs that you can check online and the one most suitable for you.