Run on an on-prem cluster (expert)¶
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:
import os
from pytorch_lightning.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()])
Get help¶
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:
