Conventions used
There are a number of text conventions used throughout this book.
Code in text
: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "And because batch_size
is now coupled with world_size
, we provide it as an input argument for an easier training interface."
A block of code is set as follows:
# define the optimization schedule for both G and D opt_gen = torch.optim.Adam(gen.parameters(), lr=lrate) opt_disc = torch.optim.Adam(disc.parameters(), lr=lrate)
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
def main(): parser.add_argument('--num-gpu-processes', default=1, type=int) args.world_size = args.num_gpu_processes * args.num_machines mp.spawn(train, nprocs=args.num_gpu_processes, args=(args,))
Any command-line input or output is written as follows:
jupyter==1.0.0 torch==1.4.0 torchvision==0.5.0 matplotlib==3.1.2 pytorch-lightning==1.0.5 fastai==2.1.8
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "First, the random noise input vector of size 64 is reshaped and projected into 128 feature maps of size 16x16 each."
Tips or important notes
Appear like this.