Introduction
To support interactive computing, fastai provides easy access to commonly-used external modules. A star import such as:
from fastai.basics import *
will populate the current namespace with these external modules in addition to fastai-specific functions and variables. This page documents these convenience imports, which are defined in fastai.imports.
Note: since this document was manually created, it could be outdated by the time you read it. To get the up-to-date listing of imports, use:
python -c 'a = set([*vars().keys(), "a"]); from fastai.basics import *; print(*sorted(set(vars().keys())-a), sep="\n")'
Names in bold are modules. If an object was aliased during its import, the original name is listed in parentheses.
| Name | Description |
|---|---|
csv |
CSV file reading and writing |
gc |
Garbage collector interface |
gzip |
Support for gzip files |
os |
Miscellaneous operating system interfaces |
pickle |
Python object serialization |
shutil |
High level file operations |
sys |
System-specific parameters and functions |
warnings, warn |
Warning control |
yaml |
YAML parser and emitter |
io, BufferedWriter, BytesIO |
Core tools for working with streams |
subprocess |
Subprocess management |
math |
Mathematical functions |
plt (matplotlib.pyplot) |
MATLAB-like plotting framework |
np (numpy) , array, cos, exp,log, sin, tan, tanh |
Multi-dimensional arrays, mathematical functions |
pd (pandas), Series, DataFrame |
Data structures and tools for data analysis |
random |
Generate pseudo-random numbers |
scipy.stats |
Statistical functions |
scipy.special |
Special functions |
abstractmethod, abstractproperty |
Abstract base classes |
collections, Counter, defaultdict, namedtuple, OrderedDict |
Container datatypes |
abc (collections.abc), Iterable |
Abstract base classes for containers |
hashlib |
Secure hashes and message digests |
itertools |
Functions creating iterators for efficient looping |
json |
JSON encoder and decoder |
operator, attrgetter, itemgetter |
Standard operators as functions |
pathlib, Path |
Object-oriented filesystem paths |
mimetypes |
Map filenames to MIME types |
inspect |
Inspect live objects |
typing, Any, AnyStr, Callable,Collection, Dict, Hashable, Iterator,List, Mapping, NewType, Optional,Sequence, Tuple, TypeVar, Union |
Support for type hints |
functools, partial, reduce |
Higher-order functions and operations on callable objects |
importlib |
The implementatin of import |
weakref |
Weak references |
html |
HyperText Markup Language support |
re |
Regular expression operations |
requests |
HTTP for Humans™ |
tarfile |
Read and write tar archive files |
numbers, Number |
Numeric abstract base classes |
tempfile |
Generate temporary files and directories |
concurrent, ProcessPoolExecutor,ThreadPoolExecutor |
Launch parallel tasks |
copy, deepcopy |
Shallow and deep copy operation |
dataclass, field, InitVar |
Data Classes |
Enum, IntEnum |
Support for enumerations |
set_trace |
The Python debugger |
patches (matplotlib.patches), Patch |
? |
patheffects (matplotlib.patheffects) |
? |
contextmanager |
Utilities for with-statement contexts |
MasterBar, master_bar, ProgressBar,progress_bar |
Simple and flexible progress bar for Jupyter Notebook and console |
pkg_resources |
Package discovery and resource access |
SimpleNamespace |
Dynamic type creation and names for built-in types |
torch, as_tensor, ByteTensor,DoubleTensor, FloatTensor, HalfTensor,LongTensor, ShortTensor, Tensor |
Tensor computation and deep learning |
nn (torch.nn), weight_norm, spectral_norm |
Neural networks with PyTorch |
F (torch.nn.functional) |
PyTorch functional interface |
optim (torch.optim) |
Optimization algorithms in PyTorch |
BatchSampler, DataLoader, Dataset,Sampler, TensorDataset |
PyTorch data utils |