--eval_steps: Number of steps in evaluation. By default, eval will stop after eval_steps or when it runs through the eval dataset once in full, whichever comes first, so this can be a very large number. (default: '100')

--output_dir: Base output directory for run. (default: '')

--t2t_usr_dir: Path to a Python module that will be imported. The __init__.py file should include the necessary imports. The imported files should contain registrations, e.g. @registry.register_model calls, that will then be available to the t2t-trainer.

--keep_checkpoint_every_n_hours: Number of hours between each checkpoint to be saved. The default value 10,000 hours effectively disables it. (default: '10000') (an integer)

--keep_checkpoint_max: How many recent checkpoints to keep. (default: '20') (an integer)

--local_eval_frequency: Save checkpoints and run evaluation every N steps during local training. (default: '1000') (an integer)

--train_steps: The number of steps to run training for. (default: '250000') (an integer)

--warm_start_from: Warm start from checkpoint.

--worker_gpu: How many GPUs to use. (default: '1') (an integer)