Run on an on-prem cluster (intermediate)¶

Audience: Users who need to run on an academic or enterprise private cluster.

Set up the cluster¶

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:

Multiple computers with PyTorch Lightning installed
A network connectivity between them with firewall rules that allow traffic flow on a specified MASTER_PORT.
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. 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; the total number of GPUs/processes that you will use
NODE_RANK - required; id of the node in the cluster

Set up the training script¶

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

Design your LightningModule (no need to add anything specific here).

Enable DDP in the trainer

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

Submit a job to the cluster¶

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:

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

Debug on a cluster¶

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.

NCCL_DEBUG=INFO python train.py ...