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Run on an on-prem cluster (intermediate)
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**Audience**: Users who need to run on an academic or enterprise private cluster.

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.. _non-slurm:

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Setup the cluster
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This guide shows how to run a training job on a general purpose cluster. We recommend beginners to try this method
first because it requires the least amount of configuration and changes to the code.
To setup a multi-node computing cluster you need:

1) Multiple computers with PyTorch Lightning installed
2) A network connectivity between them with firewall rules that allow traffic flow on a specified *MASTER_PORT*.
3) Defined environment variables on each node required for the PyTorch Lightning multi-node distributed training

PyTorch Lightning follows the design of `PyTorch distributed communication package <https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization>`_. and requires the following environment variables to be defined on each node:

- *MASTER_PORT* - required; has to be a free port on machine with NODE_RANK 0
- *MASTER_ADDR* - required (except for NODE_RANK 0); address of NODE_RANK 0 node
- *WORLD_SIZE* - required; how many nodes are in the cluster
- *NODE_RANK* - required; id of the node in the cluster

.. _training_script_setup:

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Setup the training script
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To train a model using multiple nodes, do the following:

1.  Design your :ref:`lightning_module` (no need to add anything specific here).

2.  Enable DDP in the trainer

    .. code-block:: python

       # train on 32 GPUs across 4 nodes
       trainer = Trainer(accelerator="gpu", devices=8, num_nodes=4, strategy="ddp")

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Submit a job to the cluster
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To submit a training job to the cluster you need to run the same training script on each node of the cluster.
This means that you need to:

1. Copy all third-party libraries to each node (usually means - distribute requirements.txt file and install it).
2. Copy all your import dependencies and the script itself to each node.
3. Run the script on each node.

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Debug on a cluster
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When running in DDP mode, some errors in your code can show up as an NCCL issue.
Set the ``NCCL_DEBUG=INFO`` environment variable to see the ACTUAL error.

.. code-block:: bash

    NCCL_DEBUG=INFO python train.py ...

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Get help
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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.

Try it out for free today:

.. raw:: html

    <div class="display-card-container">
        <div class="row">

.. Add callout items below this line

.. displayitem::
   :header: Train models on the cloud
   :description: Learn to run a model in the background on a cloud machine.
   :col_css: col-md-6
   :button_link: cloud_training.html
   :height: 150
   :tag: intermediate

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        </div>
    </div
