Train script
-------------

If your train script works with ``torch.distributed.launch`` it will continue
working with ``torch.distributed.run`` with these differences:

1. No need to manually pass ``RANK``, ``WORLD_SIZE``,
   ``MASTER_ADDR``, and ``MASTER_PORT``.

2. ``rdzv_backend`` and ``rdzv_endpoint`` must be provided. For most users
   this will be set to ``c10d`` (see `rendezvous <rendezvous.html>`_).

3. Make sure you have a ``load_checkpoint(path)`` and
   ``save_checkpoint(path)`` logic in your script. When workers fail
   we restart all the workers with the same program arguments so you will
   lose progress up to the most recent checkpoint
   (see `elastic launch <distributed.html>`_).

4. ``use_env`` flag has been removed. If you were parsing local rank by parsing
   the ``--local_rank`` option, you need to get the local rank from the
   environment variable ``LOCAL_RANK`` (e.g. ``os.environ["LOCAL_RANK"]``).

Below is an expository example of a training script that checkpoints on each
epoch, hence the worst-case progress lost on failure is one full epoch worth
of training.

.. code-block:: python

  def main():
       args = parse_args(sys.argv[1:])
       state = load_checkpoint(args.checkpoint_path)
       initialize(state)

       # torch.distributed.run ensure that this will work
       # by exporting all the env vars needed to initialize the process group
       torch.distributed.init_process_group(backend=args.backend)

       for i in range(state.epoch, state.total_num_epochs)
            for batch in iter(state.dataset)
                train(batch, state.model)

            state.epoch += 1
            save_checkpoint(state)

For concrete examples of torchelastic-compliant train scripts, visit
our `examples <examples.html>`_ page.