67 lines
2.3 KiB
ReStructuredText
67 lines
2.3 KiB
ReStructuredText
########################################
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Run on an on-prem cluster (intermediate)
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########################################
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.. _torch_distributed_run:
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*************************
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Run with TorchDistributed
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*************************
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`Torch Distributed Run <https://pytorch.org/docs/stable/elastic/run.html>`__ provides helper functions to setup distributed environment variables from the `PyTorch distributed communication package <https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization>`__ that need to be defined on each node.
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Once the script is setup like described in :ref:` Training Script Setup<training_script_setup>`, you can run the below command across your nodes to start multi-node training.
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Like a custom cluster, you have to ensure that there is network connectivity between the nodes with firewall rules that allow traffic flow on a specified *MASTER_PORT*.
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Finally, you'll need to decide which node you'd like to be the main node (*MASTER_ADDR*), and the ranks of each node (*NODE_RANK*).
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For example:
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* *MASTER_ADDR* 10.10.10.16
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* *MASTER_PORT* 29500
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* *NODE_RANK* 0 for the first node, 1 for the second node
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Run the below command with the appropriate variables set on each node.
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.. code-block:: bash
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python -m torch.distributed.run
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--nnodes=2 # number of nodes you'd like to run with
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--master_addr <MASTER_ADDR>
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--master_port <MASTER_PORT>
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--node_rank <NODE_RANK>
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train.py (--arg1 ... train script args...)
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.. note::
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``torch.distributed.run`` assumes that you'd like to spawn a process per GPU if GPU devices are found on the node. This can be adjusted with ``-nproc_per_node``.
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----
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********
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Get help
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********
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Setting up a cluster for distributed training is not trivial. Lightning offers lightning-grid which allows you to configure a cluster easily and run experiments via the CLI and web UI.
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Try it out for free today:
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.. raw:: html
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<div class="display-card-container">
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<div class="row">
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.. Add callout items below this line
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.. displayitem::
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:header: Train models on the cloud
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:description: Learn to run a model in the background on a cloud machine.
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:col_css: col-md-6
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:button_link: cloud_training.html
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:height: 150
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:tag: intermediate
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.. raw:: html
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</div>
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</div
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