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Notebook Launcher
Launch your training function inside a notebook. Currently supports launching a training with TPUs on Google Colab and Kaggle kernels, as well as training on several GPUs (if the machine on which you are running your notebook has them).
An example can be found in this notebook.
Your Accelerator object should only be defined inside the training function. This is because the
initialization should be done inside the launcher only.
accelerate.notebook_launcher
< source >( function args = () num_processes = None use_fp16 = False mixed_precision = 'no' use_port = '29500' )
Parameters
-
function (
Callable) — The training function to execute. If it accepts arguments, the first argument should be the index of the process run. -
args (
Tuple) — Tuple of arguments to pass to the function (it will receive*args). -
num_processes (
int, optional) — The number of processes to use for training. Will default to 8 in Colab/Kaggle if a TPU is available, to the number of GPUs available otherwise. -
mixed_precision (
str, optional, defaults to"no") — Iffp16orbf16, will use mixed precision training on multi-GPU. -
use_port (
str, optional, defaults to"29500") — The port to use to communicate between processes when launching a multi-GPU training.
Launches a training function, using several processes if it’s possible in the current environment (TPU with multiple cores for instance).