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Edit: /usr/local/lib64/python3.6/site-packages/torch/distributed/__pycache__/run.cpython-36.pyc (23317B)
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``torchrun`` provides a superset of the functionality as ``torch.distributed.launch``
with the following additional functionalities:
1. Worker failures are handled gracefully by restarting all workers.
2. Worker ``RANK`` and ``WORLD_SIZE`` are assigned automatically.
3. Number of nodes is allowed to change between minimum and maximum sizes (elasticity).
Transitioning from torch.distributed.launch to torchrun
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
``torchrun`` supports the same arguments as ``torch.distributed.launch`` **except**
for ``--use_env`` which is now deprecated. To migrate from ``torch.distributed.launch``
to ``torchrun`` follow these steps:
1. If your training script is already reading ``local_rank`` from the ``LOCAL_RANK`` environment variable.
Then you need simply omit the ``--use_env`` flag, e.g.:
+--------------------------------------------------------------------+--------------------------------------------+
| ``torch.distributed.launch`` | ``torchrun`` |
+====================================================================+============================================+
| | |
| .. code-block:: shell-session | .. code-block:: shell-session |
| | |
| $ python -m torch.distributed.launch --use_env train_script.py | $ torchrun train_script.py |
| | |
+--------------------------------------------------------------------+--------------------------------------------+
2. If your training script reads local rank from a ``--local_rank`` cmd argument.
Change your training script to read from the ``LOCAL_RANK`` environment variable as
demonstrated by the following code snippet:
+-------------------------------------------------------+----------------------------------------------------+
| ``torch.distributed.launch`` | ``torchrun`` |
+=======================================================+====================================================+
| | |
| .. code-block:: python | .. code-block:: python |
| | |
| | |
| import argparse | import os |
| parser = argparse.ArgumentParser() | local_rank = int(os.environ["LOCAL_RANK"]) |
| parser.add_argument("--local_rank", type=int) | |
| args = parser.parse_args() | |
| | |
| local_rank = args.local_rank | |
| | |
+-------------------------------------------------------+----------------------------------------------------+
The aformentioned changes suffice to migrate from ``torch.distributed.launch`` to ``torchrun``.
To take advantage of new features such as elasticity, fault-tolerance, and error reporting of ``torchrun``
please refer to:
* :ref:`elastic_train_script` for more information on authoring training scripts that are ``torchrun`` compliant.
* the rest of this page for more information on the features of ``torchrun``.
Usage
~~~~~~
1. Single-node multi-worker
::
>>> torchrun
--standalone
--nnodes=1
--nproc_per_node=$NUM_TRAINERS
YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
2. Fault tolerant (fixed sized number of workers, no elasticity):
::
>>> torchrun
--nnodes=$NUM_NODES
--nproc_per_node=$NUM_TRAINERS
--rdzv_id=$JOB_ID
--rdzv_backend=c10d
--rdzv_endpoint=$HOST_NODE_ADDR
YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
``HOST_NODE_ADDR``, in form
[:] (e.g. node1.example.com:29400), specifies the node and
the port on which the C10d rendezvous backend should be instantiated and hosted. It can be any
node in your training cluster, but ideally you should pick a node that has a high bandwidth.
.. note::
If no port number is specified ``HOST_NODE_ADDR`` defaults to 29400.
3. Elastic (``min=1``, ``max=4``):
::
>>> torchrun
--nnodes=1:4
--nproc_per_node=$NUM_TRAINERS
--rdzv_id=$JOB_ID
--rdzv_backend=c10d
--rdzv_endpoint=$HOST_NODE_ADDR
YOUR_TRAINING_SCRIPT.py (--arg1 ... train script args...)
``HOST_NODE_ADDR``, in form [:] (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 on rendezvous backend**:
For multi-node training you need to specify:
1. ``--rdzv_id``: A unique job id (shared by all nodes participating in the job)
2. ``--rdzv_backend``: An implementation of
:py:class:`torch.distributed.elastic.rendezvous.RendezvousHandler`
3. ``--rdzv_endpoint``: The endpoint where the rendezvous backend is running; usually in form
``host:port``.
Currently ``c10d`` (recommended), ``etcd-v2``, and ``etcd`` (legacy) rendezvous backends are
supported out of the box. To use ``etcd-v2`` or ``etcd``, setup an etcd server with the ``v2`` api
enabled (e.g. ``--enable-v2``).
.. warning::
``etcd-v2`` and ``etcd`` rendezvous use etcd API v2. You MUST enable the v2 API on the etcd
server. Our tests use etcd v3.4.3.
.. warning::
For etcd-based rendezvous we recommend using ``etcd-v2`` over ``etcd`` which is functionally
equivalent, but uses a revised implementation. ``etcd`` is in maintenance mode and will be
removed in a future version.
**Definitions:**
1. ``Node`` - A physical instance or a container; maps to the unit that the job manager works with.
2. ``Worker`` - A worker in the context of distributed training.
3. ``WorkerGroup`` - The set of workers that execute the same function (e.g. trainers).
4. ``LocalWorkerGroup`` - A subset of the workers in the worker group running on the same node.
5. ``RANK`` - The rank of the worker within a worker group.
6. ``WORLD_SIZE`` - The total number of workers in a worker group.
7. ``LOCAL_RANK`` - The rank of the worker within a local worker group.
8. ``LOCAL_WORLD_SIZE`` - The size of the local worker group.
9. ``rdzv_id`` - A user-defined id that uniquely identifies the worker group for a job. This id is
used by each node to join as a member of a particular worker group.
9. ``rdzv_backend`` - The backend of the rendezvous (e.g. ``c10d``). This is typically a strongly
consistent key-value store.
10. ``rdzv_endpoint`` - The rendezvous backend endpoint; usually in form ``:``.
A ``Node`` runs ``LOCAL_WORLD_SIZE`` workers which comprise a ``LocalWorkerGroup``. The union of
all ``LocalWorkerGroups`` in the nodes in the job comprise the ``WorkerGroup``.
**Environment Variables:**
The following environment variables are made available to you in your script:
1. ``LOCAL_RANK`` - The local rank.
2. ``RANK`` - The global rank.
3. ``GROUP_RANK`` - The rank of the worker group. A number between 0 and ``max_nnodes``. When
running a single worker group per node, this is the rank of the node.
4. ``ROLE_RANK`` - The rank of the worker across all the workers that have the same role. The role
of the worker is specified in the ``WorkerSpec``.
5. ``LOCAL_WORLD_SIZE`` - The local world size (e.g. number of workers running locally); equals to
``--nproc_per_node`` specified on ``torchrun``.
6. ``WORLD_SIZE`` - The world size (total number of workers in the job).
7. ``ROLE_WORLD_SIZE`` - The total number of workers that was launched with the same role specified
in ``WorkerSpec``.
8. ``MASTER_ADDR`` - The FQDN of the host that is running worker with rank 0; used to initialize
the Torch Distributed backend.
9. ``MASTER_PORT`` - The port on the ``MASTER_ADDR`` that can be used to host the C10d TCP store.
10. ``TORCHELASTIC_RESTART_COUNT`` - The number of worker group restarts so far.
11. ``TORCHELASTIC_MAX_RESTARTS`` - The configured maximum number of restarts.
12. ``TORCHELASTIC_RUN_ID`` - Equal to the rendezvous ``run_id`` (e.g. unique job id).
**Deployment:**
1. (Not needed for the C10d backend) Start the rendezvous backend server and get the endpoint (to be
passed as ``--rdzv_endpoint`` to the launcher script)
2. Single-node multi-worker: Start the launcher on the host to start the agent process which
creates and monitors a local worker group.
3. Multi-node multi-worker: Start the launcher with the same arguments on all the nodes
participating in training.
When using a job/cluster manager the entry point command to the multi-node job should be this
launcher.
**Failure Modes:**
1. Worker failure: For a training job with ``n`` workers, if ``k<=n`` workers fail all workers
are stopped and restarted up to ``max_restarts``.
2. Agent failure: An agent failure results in a local worker group failure. It is up to the job
manager to fail the entire job (gang semantics) or attempt to replace the node. Both behaviors
are supported by the agent.
3. Node failure: Same as agent failure.
**Membership Changes:**
1. Node departure (scale-down): The agent is notified of the departure, all existing workers are
stopped, a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
``WORLD_SIZE``.
2. Node arrival (scale-up): The new node is admitted to the job, all existing workers are stopped,
a new ``WorkerGroup`` is formed, and all workers are started with a new ``RANK`` and
``WORLD_SIZE``.
**Important Notices:**
1. This utility and multi-process distributed (single-node or
multi-node) GPU training currently only achieves the best performance using
the NCCL distributed backend. Thus NCCL backend is the recommended backend to
use for GPU training.
2. The environment variables necessary to initialize a Torch process group are provided to you by
this module, no need for you to pass ``RANK`` manually. To initialize a process group in your
training script, simply run:
::
>>> import torch.distributed as dist
>>> dist.init_process_group(backend="gloo|nccl")
3. In your training program, you can either use regular distributed functions
or use :func:`torch.nn.parallel.DistributedDataParallel` module. If your
training program uses GPUs for training and you would like to use
:func:`torch.nn.parallel.DistributedDataParallel` module,
here is how to configure it.
::
local_rank = int(os.environ["LOCAL_RANK"])
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[local_rank],
output_device=local_rank)
Please ensure that ``device_ids`` argument is set to be the only GPU device id
that your code will be operating on. This is generally the local rank of the
process. In other words, the ``device_ids`` needs to be ``[int(os.environ("LOCAL_RANK"))]``,
and ``output_device`` needs to be ``int(os.environ("LOCAL_RANK"))`` in order to use this
utility
4. On failures or membership changes ALL surviving workers are killed immediately. Make sure to
checkpoint your progress. The frequency of checkpoints should depend on your job's tolerance
for lost work.
5. This module only supports homogeneous ``LOCAL_WORLD_SIZE``. That is, it is assumed that all
nodes run the same number of local workers (per role).
6. ``RANK`` is NOT stable. Between restarts, the local workers on a node can be assgined a
different range of ranks than before. NEVER hard code any assumptions about the stable-ness of
ranks or some correlation between ``RANK`` and ``LOCAL_RANK``.
7. When using elasticity (``min_size!=max_size``) DO NOT hard code assumptions about
``WORLD_SIZE`` as the world size can change as nodes are allowed to leave and join.
8. It is recommended for your script to have the following structure:
::
def main():
load_checkpoint(checkpoint_path)
initialize()
train()
def train():
for batch in iter(dataset):
train_step(batch)
if should_checkpoint:
save_checkpoint(checkpoint_path)
9. (Recommended) On worker errors, this tool will summarize the details of the error
(e.g. time, rank, host, pid, traceback, etc). On each node, the first error (by timestamp)
is heuristically reported as the "Root Cause" error. To get tracebacks as part of this
error summary print out, you must decorate your main entrypoint function in your
training script as shown in the example below. If not decorated, then the summary
will not include the traceback of the exception and will only contain the exitcode.
For details on torchelastic error handling see: https://pytorch.org/docs/stable/elastic/errors.html
::
from torch.distributed.elastic.multiprocessing.errors import record
@record
def main():
# do train
pass
if __name__ == "__main__":
main()
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