Quickstart
===========

To launch a **fault-tolerant** job, run the following on all nodes.

.. code-block:: bash

    torchrun
       --nnodes=NUM_NODES
       --nproc_per_node=TRAINERS_PER_NODE
       --max_restarts=NUM_ALLOWED_FAILURES
       --rdzv_id=JOB_ID
       --rdzv_backend=c10d
       --rdzv_endpoint=HOST_NODE_ADDR
       YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)


To launch an **elastic** job, run the following on at least ``MIN_SIZE`` nodes
and at most ``MAX_SIZE`` nodes.

.. code-block:: bash

    torchrun
        --nnodes=MIN_SIZE:MAX_SIZE
        --nproc_per_node=TRAINERS_PER_NODE
        --max_restarts=NUM_ALLOWED_FAILURES_OR_MEMBERSHIP_CHANGES
        --rdzv_id=JOB_ID
        --rdzv_backend=c10d
        --rdzv_endpoint=HOST_NODE_ADDR
        YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)

.. note::
   TorchElastic models failures as membership changes. When a node fails,
   this is treated as a "scale down" event. When the failed node is replaced by
   the scheduler, it is a "scale up" event. Hence for both fault tolerant
   and elastic jobs, ``--max_restarts`` is used to control the total number of
   restarts before giving up, regardless of whether the restart was caused
   due to a failure or a scaling event.

``HOST_NODE_ADDR``, in form <host>[:<port>] (e.g. node1.example.com:29400),
specifies the node and the port on which the C10d rendezvous backend should be
instantiated and hosted. It can be any node in your training cluster, but
ideally you should pick a node that has a high bandwidth.

.. note::
   If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.

.. note::
   The ``--standalone`` option can be passed to launch a single node job with a
   sidecar rendezvous backend. You don’t have to pass ``--rdzv_id``,
   ``--rdzv_endpoint``, and ``--rdzv_backend`` when the ``--standalone`` option
   is used.


.. note::
   Learn more about writing your distributed training script
   `here <train_script.html>`_.

If ``torchrun`` does not meet your requirements you may use our APIs directly
for more powerful customization. Start by taking a look at the
`elastic agent <agent.html>`_ API.