Troubleshooting Distributed Data Parallel (DDP) Setup Across Multiple Hosts Using SLURM and Torchrun

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Resolving Issues with DDP on Multi-Host Environments via SLURM and Torchrun

When attempting to implement Distributed Data Parallel (DDP) across two or more hosts while utilizing SLURM together with torchrun, you might encounter errors. This guide is dedicated to addressing those challenges.

What You’ll Learn

In this tutorial, you will explore how to effectively troubleshoot and resolve issues encountered when wrapping DDP around two hosts using SLURM in conjunction with torchrun.

Introduction to Problem and Solution

Distributed training can significantly speed up your model’s training time by leveraging multiple GPUs across several nodes. However, setting it up can be quite challenging, especially when using specific tools like SLURM for job scheduling and torchrun for initiating the distributed process. The main issue often arises from misconfigurations that prevent effective communication between the different nodes involved in the process.

To overcome these challenges, we will follow a structured approach: 1. Verify PyTorch installation supports DDP. 2. Configure network settings for seamless node communication. 3. Set up SLURM scripts correctly for launching jobs across multiple hosts. 4. Troubleshoot common errors like connection timeouts or configuration mismatches.

Code

# Example code snippet for initializing a basic DDP setup
import torch
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'

    # Initializes the default distributed process group
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

def cleanup():
    dist.destroy_process_group()

def demo_basic(rank, world_size):
    setup(rank, world_size)

    # Example model
    model = MyModel().to(rank)
    ddp_model = DDP(model, device_ids=[rank])

    # Training loop goes here

    cleanup()

# Copyright PHD

Solution Implementation

To execute this script across multiple nodes managed by SLURM:

  1. Ensure NCCL debugging is enabled: This helps in identifying communication issues.
  2. Use a shared filesystem: All nodes must have access to the script files.
  3. SLURM job script:
    • Use torch.distributed.launch or torchrun depending on your PyTorch version.
    • Utilize sbatch command with proper flags for specifying the number of GPUs per node.

Detailed Explanation of Each Step

The provided code initializes a simple DDP scenario where each GPU on each node runs its instance of an example model wrapped in DistributedDataParallel. Key steps include: – Setting environment variables like MASTER_ADDR and MASTER_PORT. – Initializing PyTorch’s default process group which manages backend communications via NCCL. – Wrapping your model with DistributedDataParallel.

This framework ensures efficient parallelism over distributed systems by synchronizing gradients globally while operating on independent data slices per process.

  1. Can I use Ethernet instead of InfiniBand?

  2. Yes, but performance may be impacted due to higher latencies compared with InfiniBand networks commonly used in high-performance computing environments.

  3. How do I solve “Connection refused” errors?

  4. Check if firewalls are blocking ports needed for node-to-node communication or if there’s an issue with SSH configurations allowing seamless connections between allocated nodes under SLURM’s control.

  5. Do I need identical GPUs across all nodes?

  6. While not strictly required thanks to mixed precision training techniques available today; having homogeneous hardware simplifies configuration and optimizes performance consistency.

  7. How many workers per GPU should I configure?

  8. It depends on your dataset size and model architecture; however starting with 1 worker per GPU then adjusting based on CPU utilization rates is a good strategy.

  9. Is there a difference between using torch.distributed.launch vs.torchrun?

  10. Functionality-wise they aim towards similar goals; however newer versions encourage using torchrun due its simpler interface.

  11. Can I run mixed precision training using DDP?

  12. Yes, PyTorch supports Automatic Mixed Precision (AMP) which can easily integrate within a DPP setup improving both memory usage efficiency & potentially speeding up computation times.

  13. Conclusion

  14. Setting up Distributed Data Parallel processing across multiple hosts requires careful consideration regarding hardware compatibility network configurations alongside precise execution employing tools like SLUMR & TorchRun ensuring smooth operation eliminating bottlenecks associated either infrastructure misconfigurations software limitations inherent complex nature multi-host setups patience diligence remain paramount successful deployment scalable machine learning applications harnessing true power parallel computation.

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