Note: To get started with fastai, have a look at the training overview. The documentation below covers some lower-level details.

Core modules of fastai

The basic foundations needed in several parts of the library are provided by these modules:

basic_data

This module defines the basic DataBunch class which is what will be needed to create a Learner object with a model. It also defines the DeviceDataLoader, a class that wraps a pytorch DataLoader to put batches on the right device.

layers

This module contains the definitions of basic custom layers we need in most of our models, as well as a few helper functions to create simple blocks.

Most of the documentation of the following two modules can be skipped at a first read, unless you specifically want to know what a certain function is doing.

core

This module contains the most basic functions and imports, notably:

• pandas as pd
• numpy as np
• matplotlib.pyplot as plt

torch_core

This module contains the most basic functions and imports that use pytorch. We follow pytorch naming conventions, mainly:

• torch.nn as nn
• torch.optim as optim
• torch.nn.functional as F

Usage

Core modules are designed to be in conjuction with application specific modules and imported automatically in those cases.

To import core functionality only:

from fastai.basics import *