PyTorch for Enterprise AI: Architecture & Integration

import os
import torch
import torch.nn as nn
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP

def setup_ddp():
  dist.init_process_group(backend="nccl")
  local_rank = int(os.environ["LOCAL_RANK"])
  torch.cuda.set_device(local_rank)
  return local_rank

def train_production_step():
  local_rank = setup_ddp()
  
  # Enable expandable segment memory allocator to eliminate VRAM fragmentation
  os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

  # Define model and move to target GPU
  model = nn.Sequential(
      nn.Linear(4096, 8192),
      nn.GELU(),
      nn.Linear(8192, 4096)
  ).to(local_rank)

  # Wrap model with DistributedDataParallel and PyTorch 2.x compiler
  model = DDP(model, device_ids=[local_rank])
  compiled_model = torch.compile(model, mode="max-autotune")

  optimizer = torch.optim.AdamW(compiled_model.parameters(), lr=1e-4)

  # Production forward pass with bfloat16 AMP
  inputs = torch.randn(32, 4096, device=local_rank)
  with torch.cuda.amp.autocast(dtype=torch.bfloat16):
      outputs = compiled_model(inputs)
      loss = outputs.pow(2).mean()

  optimizer.zero_grad()
  loss.backward()
  optimizer.step()
  
  if local_rank == 0:
      print(f"Step Loss: {loss.item():.4f}")

if __name__ == "__main__":
  train_production_step()