Fastai is a comprehensive deep learning library built on top of PyTorch, designed to simplify the process of creating and training neural networks. Developed by Jeremy Howard and Rachel Thomas, Fastai is part of a broader mission to democratize deep learning. It provides high-level components that make deep learning more accessible, enabling users with varying levels of experience to efficiently develop and deploy AI models. The library’s design emphasizes practicality, allowing users to achieve state-of-the-art results with minimal code.

Fastai’s impact is further amplified through its commitment to accessibility and openness. As a self-funded research, software development, and teaching lab, Fastai releases all its software, research papers, and courses for free, ensuring independence and wide availability to a global audience. The library is a product of ongoing research and innovation in deep learning, continually refined and enhanced to incorporate the latest best practices and advancements in the field.

Core Components of Fastai

1. Fastai Library

The Fastai library is built on top of the PyTorch framework and is intended to make deep learning more approachable. It provides a high-level API that abstracts many complexities associated with deep learning, allowing users to focus on application-specific tasks rather than intricate programming details. This abstraction is achieved through a layered API design, which provides both high-level and low-level components for various deep learning tasks. The library’s functionality extends from vision and text processing to tabular data and collaborative filtering.

Fastai v2 represents a complete rewrite aimed at enhancing speed, flexibility, and ease of use. It is built on foundational libraries like fastcore and fastgpu, which support the high-level components of Fastai and are useful for many programmers and data scientists.

2. Layered API

Fastai’s layered API is structured to cater to different user needs—from beginners to advanced researchers. The high-level API offers ready-to-use functions for training models across several domains, including vision, text, tabular data, and collaborative filtering. These functions come with sensible defaults that streamline the model-building process. The mid-level API provides core methods for deep learning and data processing, whereas the low-level API offers optimized primitives and foundations for customized model development.

A key feature of Fastai’s layered architecture is its ability to express common underlying patterns of many deep learning and data processing techniques through decoupled abstractions. This is made possible by leveraging the dynamism of Python and the flexibility of PyTorch, allowing for a balance between ease of use, flexibility, and performance.

3. Practical Deep Learning for Coders Course

Fastai offers a free, massive open online course (MOOC) titled “Practical Deep Learning for Coders.” This course provides hands-on experience with deep learning, using only Python knowledge as a prerequisite. It covers various deep learning topics such as image classification, natural language processing (NLP), and generative adversarial networks (GANs), with a focus on real-world applications. The course is complemented by the book “Deep Learning for Coders with Fastai and PyTorch,” which delves into AI applications without requiring a PhD.

Key Features of Fastai

1. High-Level API

Fastai’s high-level API simplifies the training of deep learning models by integrating architecture, data, and optimization into a cohesive system. It automatically selects appropriate loss functions and optimization algorithms, reducing the likelihood of user errors. This API is particularly useful for beginners and those interested in applying existing deep learning methods without delving into the complexities of the underlying code.

2. Transfer Learning

One of the standout features of Fastai is its support for transfer learning, which enables models to be trained quickly and efficiently by leveraging pre-trained networks. Fastai optimizes this process through techniques such as batch-normalization, layer freezing, and discriminative learning rates. These techniques accelerate the learning process and enhance model performance across different tasks.

3. Data Block API

The data block API is a flexible system for defining data processing pipelines. It allows users to specify data sources, transformations, and loading mechanisms in a modular fashion. This API is instrumental in managing complex datasets and ensuring that data is prepared correctly for model training. It provides a consistent approach across various domains, making it easier to apply deep learning techniques to different types of data.

4. Two-Way Callback System

Fastai introduces a novel two-way callback system that provides hooks into the training loop, enabling dynamic changes to data, models, or optimizers at any point during training. This system enhances the flexibility and customizability of model training processes, allowing for innovative approaches to be implemented with minimal coding effort.

5. Open Source and Community-Driven

Fastai is an open-source project hosted on GitHub, licensed under the Apache License 2.0. It benefits from a vibrant community of developers and researchers who contribute to its ongoing development and improvement. The library’s open-source nature ensures that users have access to cutting-edge techniques and can contribute to its evolution. Fastai’s community is also a source of collaborative learning and support for users worldwide.

Use Cases and Applications

Vision

Fastai simplifies the development of computer vision models with minimal code. For example, an ImageNet model can be fine-tuned on a specific dataset, such as the Oxford IIT Pets dataset, achieving high accuracy with just a few lines of code. The library’s integration with PyTorch ensures that users can leverage state-of-the-art architectures and techniques with ease.

Text

Fastai is equipped with tools for natural language processing (NLP), enabling tasks such as sentiment analysis, text classification, and language modeling. The library’s integration with PyTorch allows for the use of modern NLP architectures, facilitating the development of robust language models that can understand and generate human language.

Tabular Data

Fastai provides components for processing tabular data, supporting tasks like regression and classification. Its data block API is particularly useful for transforming and loading tabular datasets, ensuring that data is processed efficiently and accurately for model training. This feature is essential for applications in business analytics, healthcare, and finance, where tabular data is prevalent.

Collaborative Filtering

Collaborative filtering is another area where Fastai excels, offering tools to build recommendation systems. The library’s high-level API simplifies the creation and evaluation of collaborative filtering models, making it easier to develop personalized recommendations for users based on their preferences and behaviors.

Deployment

Fastai supports the deployment of trained models to production environments, allowing developers to integrate AI capabilities into applications seamlessly. The library’s focus on practicality ensures that deployment considerations are addressed from the outset, enabling efficient and scalable solutions for real-world applications.

Fastai in Research papers

Fastai is a deep learning library designed to provide high-level components for practitioners to achieve state-of-the-art results quickly and easily in standard deep learning domains. At the same time, it offers low-level components for researchers to craft new approaches without compromising ease of use, flexibility, or performance. This dual capability is achieved through a carefully structured architecture that leverages the dynamism of Python and the flexibility of PyTorch. The library introduces several innovative features, including a type dispatch system for Python, a GPU-optimized computer vision library, an optimizer that simplifies modern optimization algorithms, and a novel 2-way callback system. Fastai has been successfully used to create a comprehensive deep learning course, and it is widely employed in research, industry, and teaching. For more details, you can access the paper on arXiv.

In the field of agriculture, Fastai has been effectively applied to build a classification model for plant disease recognition using a dataset of apple leaf images. By utilizing a pre-trained ResNet34 model with the Fastai framework, the study achieved a classification accuracy of 93.765%. This demonstrates the library’s capability to save training time and enhance model performance in practical applications. More information can be found in the related study on arXiv.

Additionally, Fastai’s callback system has been leveraged in other libraries such as FasterAI, which focuses on neural network compression techniques like sparsification and pruning. This demonstrates Fastai’s versatility and ease of integration with other tools to perform state-of-the-art techniques efficiently. Details of this implementation are available in the FasterAI paper on arXiv.

Examples of Usage

Installation

To start using fastai, you can easily install it using either conda or pip. Here’s how:

• Conda: Recommended for Linux or Windows.
  conda install -c fastai fastai
• Pip: Can be used across different platforms.
  pip install fastai
• Google Colab: Use fastai without installation by leveraging Google Colab, which supports GPU runtime for faster computations.

Additionally, users can explore fastai’s official documentation and community forums for troubleshooting common installation issues or to seek solutions for specific setup configurations. The library’s integration with Jupyter notebooks and Google Colab further enhances its usability by providing an interactive environment for experimentation and learning.

High-Level API

The high-level API in fastai is designed for ease of use and rapid development. It abstracts much of the complexity, allowing users to focus on building and training models with minimal code. The API is particularly beneficial for quickly prototyping models and iterating on ideas without delving into the low-level details of the underlying PyTorch framework.

Examples of High-Level API Usage

1. Image Classification:
   
   from fastai.vision.all import *

path = untar_data(URLs.PETS)

dls = ImageDataLoaders.from_name_re(path=path, fnames=get_image_files(path/"images"), pat=r'/([^/]+)_\d+.jpg$', item_tfms=RandomResizedCrop(450, min_scale=0.75), batch_tfms=[*aug_transforms(size=224, max_warp=0.), Normalize.from_stats(*imagenet_stats)])

learn = cnn_learner(dls, resnet34, metrics=error_rate) learn.fit_one_cycle(4)
   
   This example demonstrates how to set up and train an image classifier on the Oxford IIT Pets dataset using fastai’s high-level API. The key steps involve data loading, augmentation, and training with a convolutional neural network (CNN).
2. Text Sentiment Analysis:
   
   from fastai.text.all import *

path = untar_data(URLs.IMDB) dls = TextDataLoaders.from_folder(path, valid='test')

learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)

learn.fine_tune(1)
   
   Here, fastai is used to build a sentiment analysis model on the IMDB dataset. The high-level API simplifies the process of creating data loaders, setting up a learner, and fine-tuning the model.
3. Tabular Data:
   
   from fastai.tabular.all import *

path = untar_data(URLs.ADULT_SAMPLE)

dls = TabularDataLoaders.from_csv(path/'adult.csv', path=path, y_names="salary", cat_names=['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race'], cont_names=['age', 'fnlwgt', 'education-num'], procs=[Categorify, FillMissing, Normalize])

learn = tabular_learner(dls, metrics=accuracy)

learn.fit_one_cycle(3)
   
   This example illustrates the use of fastai for tabular data, specifically the Adult dataset. The API allows for easy data processing, including categorification, filling missing values, and normalization, followed by training a model.

The high-level API’s design philosophy emphasizes simplicity and efficiency, enabling users to achieve significant results with minimal code. This approach is particularly advantageous for educational purposes, allowing individuals to learn deep learning concepts through hands-on practice without overwhelming complexity.

Mid-Level API

The mid-level API in fastai offers more customization possibilities than the high-level API, allowing users to alter various aspects of the training process without delving into the lowest-level details. It provides a balance between ease of use and flexibility, catering to users who need more control over their models but still appreciate the convenience of pre-defined components.

Components of the Mid-Level API

• Learner: A core component that encapsulates the model, data, and training loop. It can be customized with different architectures, optimizers, and metrics.
• Callbacks: Functions that allow extending the training loop with custom behaviors, such as saving checkpoints, adjusting learning rates, or logging metrics.

Example Usage

learn = cnn_learner(dls, resnet18, metrics=accuracy, cbs=[SaveModelCallback(monitor='accuracy')])

learn.fine_tune(2)

In this example, a callback is used to save the model with the best accuracy during training, showcasing the flexibility of the mid-level API. Such customization options are invaluable for practitioners who wish to experiment with different training regimes or integrate specific features into their models.

The mid-level API empowers users to tailor their models to specific needs while still benefiting from fastai’s robust and efficient framework. By offering a range of customizable components, it provides a bridge between the simplicity of the high-level API and the full control of the low-level API.

Low-Level API

For advanced users and researchers who need full control over their models, fastai’s low-level API provides access to the underlying PyTorch functionalities and optimized primitives. This level of the API is intended for those who wish to push the boundaries of deep learning by designing novel architectures or implementing custom training routines.

Features of the Low-Level API

• Pipeline and Transforms: Customizable data processing sequences that can be tailored to specific needs.
• Object-Oriented Tensors: Enhancements to PyTorch tensors that facilitate more intuitive manipulation.
• Optimized Operations: Efficient tensor operations leveraging PyTorch’s capabilities.

Example Usage

from fastai.data.all import *

pipe = Pipeline([PILImage.create, Resize(128), ToTensor])
img = pipe('path/to/image.jpg')

This snippet demonstrates how to create a custom data pipeline using fastai’s low-level API, applying a series of transformations to an image. The low-level API’s flexibility allows users to fine-tune every aspect of their model and data processing, enabling experimentation with cutting-edge techniques and methodologies.

The low-level API is particularly suited for research and development environments where innovation and experimentation are prioritized. By providing direct access to PyTorch’s capabilities, it empowers users to explore new ideas and push the boundaries of what is possible with deep learning.

Fastai has been widely used in various fields due to its flexibility and ease of use. Here are some examples of its applications:

1. Fastai: A Layered API for Deep Learning
   Authors: Jeremy Howard, Sylvain Gugger
   Fastai is a deep learning library designed to facilitate rapid prototyping and deployment of deep learning models. It achieves this by providing high-level components for standard deep learning tasks while allowing researchers to delve into low-level components for custom solutions. This paper details the library’s architecture, which leverages Python’s dynamism and PyTorch’s flexibility to create decoupled abstractions for deep learning tasks. Fastai’s innovations include a type dispatch system, a GPU-optimized computer vision library, a new optimizer design, and a two-way callback system, among others. The library’s ease of use and capability to produce state-of-the-art results have made it popular in research, industry, and education. Read more
2. Microvasculature Segmentation in Human BioMolecular Atlas Program (HuBMAP)
   Authors: Youssef Sultan, Yongqiang Wang, James Scanlon, Lisa D’lima
   This study utilizes Fastai for image segmentation in the Human BioMolecular Atlas Program (HuBMAP), focusing on segmenting microvascular structures in human kidneys. The methodology starts with a Fastai U-Net model, exploring alternative architectures and deep models to improve segmentation accuracy. This work highlights Fastai’s capability to serve as a foundational tool in medical imaging, offering insights into advanced segmentation techniques and their benchmarking against baseline models. Read more
3. Egglog Python: A Pythonic Library for E-graphs
   Author: Saul Shanabrook
   Although not directly about Fastai, this paper introduces Python bindings for the egglog library, emphasizing the integration of e-graph techniques into Python. This approach aligns with Fastai’s philosophy of harnessing Python’s strengths to enhance machine learning and scientific computing innovations. It illustrates how Pythonic APIs can facilitate collaboration and innovation across domains, potentially complementing Fastai’s versatility. Read more