Apply the model to an input batch. :param x: an [N x C x ...] Tensor of inputs. :param timesteps: a 1-D batch of timesteps.

Get directory that log files are being written to. will be None if there is no output directory (i.e., if you didn't call start)

Get a pre-defined beta schedule for the given name. The beta schedule library consists of beta schedules which remain similar in the lim

Load a PyTorch file without redundant fetches across MPI ranks.

The same as logkv(), but if called many times, values averaged.

Log a dictionary of key-value pairs

Generate samples from the model. :param model: the model module. :param shape: the shape of the samples, (N, C, H, W).

Usage: @profile("my_func") def my_func(): code

Scale the parameters of a module and return it.

Set logging threshold on current logger.

Setup a distributed process group.

https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse

Synchronize a sequence of Tensors across ranks from rank 0.

Compute training losses for a single timestep. :param model: the model to evaluate loss on. :param x_start: the [N x C x ...]