Fix Plan

The fix involves optimizing the distributed job configuration to reduce timeouts on ROCm.

Steps

• Increase the timeout value in the torch.distributed configuration
• Implement a retry mechanism for failed jobs
• Optimize the job execution to reduce overall execution time

Example Code

import torch.distributed as dist

# Increase timeout value
dist.init_process_group(
    backend='nccl',
    init_method='env://',
    timeout=300000  # 5 minutes
)

# Implement retry mechanism
def run_job():
    try:
        # Job execution code
        pass
    except Exception as e:
        # Retry job execution
        run_job()

# Optimize job execution
def optimize_job_execution():
    # Use torch.cuda.amp for mixed precision training
    scaler = torch.cuda.amp.GradScaler()
    # Use torch.nn.DataParallel for parallelization
    model = torch.nn.DataParallel(model)

Verification

Verify the fix by checking the job execution logs for reduced timeouts and successful job completions.

Extra Tips

• Monitor job execution metrics to identify bottlenecks
• Adjust the timeout value and retry mechanism as needed
• Consider using a more robust distributed training framework like torch.distributed.launch