---
title: Run on an on-prem cluster (expert)
description: >-
  Lightning provides an interface for providing your own definition of a cluster
  environment. It mainly consists of
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/clouds/cluster_expert'
---
# Run on an on-prem cluster (expert) {#run-on-an-on-prem-cluster-expert}

## Integrate your own cluster {#integrate-your-own-cluster}

Lightning provides an interface for providing your own definition of a cluster environment. It mainly consists of
parsing the right environment variables to access information such as world size, global and local rank (process id),
and node rank (node id). Here is an example of a custom
`ClusterEnvironment`:

```python
import os
from lightning.pytorch.plugins.environments import ClusterEnvironment

class MyClusterEnvironment(ClusterEnvironment):
    @property
    def creates_processes_externally(self) -> bool:
        """Return True if the cluster is managed (you don't launch processes yourself)"""
        return True

    def world_size(self) -> int:
        return int(os.environ["WORLD_SIZE"])

    def global_rank(self) -> int:
        return int(os.environ["RANK"])

    def local_rank(self) -> int:
        return int(os.environ["LOCAL_RANK"])

    def node_rank(self) -> int:
        return int(os.environ["NODE_RANK"])

    def main_address(self) -> str:
        return os.environ["MASTER_ADDRESS"]

    def main_port(self) -> int:
        return int(os.environ["MASTER_PORT"])

trainer = Trainer(plugins=[MyClusterEnvironment()])
```

---

**Navigation**

- Parent: [Clouds](/docs/pytorch/2.6.6/reference/clouds.md)