Multi-Node Training using SLURM

This tutorial introduces a skeleton on how to perform distributed training on multiple GPUs over multiple nodes using the SLURM workload manager available at many supercomputing centers. The code is based on our tutorial on single-node multi-GPU training. Please go there first to understand the basics if you are unfamiliar with the concepts of distributed training in .


The complete script of this tutorial can be found at examples/multi_gpu/ You can find the example *.sbatch file next to it and tune it to your needs.

A submission script to manage startup

As we are now running on multiple nodes, we can no longer use our __main__ entrypoint and start processes from there. This is where the workload manager comes in as it allows us to prepare a special *.sbatch file. This file is a standard bash script with instructions on how to setup the processes and your environment.

Our example starts with the usual shebang #!/bin/bash and special comments instructing which resources the SLURM system should reserve for our training run. Configuration of the specifics usually depends on your site (and your usage limits!). The following is a minimal example which works with a quite unrestricted configuration available to us:

#SBATCH --job-name=pyg-multinode-tutorial # identifier for the job listings
#SBATCH --output=pyg-multinode.log        # outputfile
#SBATCH --partition=gpucloud              # ADJUST this to your system
#SBATCH -N 2                              # number of nodes you want to use
#SBATCH --ntasks=4                        # number of processes to be run
#SBATCH --gpus-per-task=1                 # every process wants one GPU!
#SBATCH --gpu-bind=none                   # NCCL can't deal with task-binding...

This example will create two processes each on two nodes with each process having a single GPU reserved.

In the following part, we have to set up some environment variables for torch.distributed to properly do the rendezvous procedure. In theory you could also set those inside the process:

export MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4))
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)

If you do not want to let your script randomly open a port and listen for incoming connections, you can also use a file on your shared filesystem.

Now the only thing left to add is the execution of the training script:

srun python

Note how the :obj`python` call is prefixed with the srun command and thus --ntasks replicas will be started.

Finally, to submit the *.sbatch file itself into the work queue, use the sbatch utility in your shell:

sbatch distributed_sampling_multinode.sbatch

Using a cluster configured with pyxis-containers

If your cluster supports the pyxis plugin developed by NVIDIA, you can use a ready-to-use container that is updated each month with the latest from NVIDIA and , see here for more information. The container sets up all necessary environment variables from which you can now directly run the example using srun from your command prompt:

srun --partition=<partitionname> -N<num_nodes> --ntasks=<number of GPUS in total> --gpus-per-task=1 --gpu-bind=none --container-name=pyg-test --container-image=<image_url> --container-mounts='.:/workspace' python3

Note that --container-mounts='.:/workspace' makes the current folder (which should include the example code) available in the default startup folder workspace of the container.

If you want to eventually customize packages in the container without having access to docker (very likely on a public HPC), you can create your own image by following this tutorial.

Modifying the training script

As SLURM now takes care of creating multiple processes and we can not share any data (each process will have the full dataset loaded!), our __main__ section now has to query the environment for the process setup generated by SLURM or the pyxis container:

# Get the world size from the WORLD_SIZE variable or directly from SLURM:
world_size = int(os.environ.get('WORLD_SIZE', os.environ.get('SLURM_NTASKS')))
# Likewise for RANK and LOCAL_RANK:
rank = int(os.environ.get('RANK', os.environ.get('SLURM_PROCID')))
local_rank = int(os.environ.get('LOCAL_RANK', os.environ.get('SLURM_LOCALID')))
run(world_size, rank, local_rank)

The torch.distributed.init_process_group() function will now pick up the MASTER_ADDR from the environment:

def run(world_size: int, rank: int, local_rank: int):
    dist.init_process_group('nccl', world_size=world_size, rank=rank)

We also have to replace the usage of rank depending on whether we want to use it for node-local purposes like selecting a GPU or global tasks such as data splitting

train_idx = data.train_mask.nonzero(as_tuple=False).view(-1)
train_idx = train_idx.split(train_idx.size(0) // world_size)[rank]

while we need to assign the model to a node-local GPU and thus use local_rank:

model = SAGE(dataset.num_features, 256, dataset.num_classes).to(local_rank)
model = DistributedDataParallel(model, device_ids=[local_rank])