update dags
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@@ -1,80 +1,272 @@
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from airflow import DAG
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from airflow.providers.standard.operators.python import PythonOperator
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from airflow.operators.bash import BashOperator
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from datetime import datetime
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import os
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from airflow.operators.python import PythonOperator
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from airflow.models import Variable
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import pendulum
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from datetime import timedelta
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import json
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# --- CONFIGURATION ---
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WORLD_SIZE = 2
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MASTER_ADDR = "airflow-worker-gpu-0.airflow-worker-gpu"
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MASTER_PORT = "29500"
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NCCL_TIMEOUT = 1800
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default_args = {
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"owner": "airflow",
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'owner': 'airflow',
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'retries': 1,
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'retry_delay': timedelta(minutes=2),
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'execution_timeout': timedelta(hours=2),
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}
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# -----------------------
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# PREP
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# -----------------------
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def prepare_training():
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print("Preparing DDP training environment")
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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os.environ["NCCL_DEBUG"] = "INFO"
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return "prepared"
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def prepare_training_func():
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"""Initialize shared state for synchronization"""
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sync_state = {
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'ready_workers': [],
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'training_started': False,
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'start_time': None
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}
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Variable.set('ddp_sync_state', json.dumps(sync_state))
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print("Training preparation complete. Sync state initialized.")
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return True
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# -----------------------
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# CLEANUP
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# -----------------------
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def cleanup():
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print("Cleaning up after training")
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return "cleaned"
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def run_training_node_func(rank, world_size):
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"""Execute distributed training with synchronization"""
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import os
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import socket
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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import torch.optim as optim
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from datetime import datetime
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import time
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print(f"{'='*60}")
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print(f"Node Rank {rank}/{world_size} - Starting at {datetime.now()}")
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print(f"Hostname: {socket.gethostname()}")
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print(f"{'='*60}")
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# STEP 1: Signal that this worker is ready
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print(f"[{rank}] Signaling ready state...")
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max_wait = 300 # 5 minutes
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start_wait = time.time()
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while time.time() - start_wait < max_wait:
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try:
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sync_state = json.loads(Variable.get('ddp_sync_state', default_var='{}'))
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if rank not in sync_state.get('ready_workers', []):
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sync_state.setdefault('ready_workers', []).append(rank)
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Variable.set('ddp_sync_state', json.dumps(sync_state))
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print(f"[{rank}] Marked as ready. Ready workers: {sync_state['ready_workers']}")
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# STEP 2: Wait for all workers to be ready
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if len(sync_state.get('ready_workers', [])) == world_size:
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print(f"[{rank}] All {world_size} workers are ready! Proceeding to training...")
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break
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print(f"[{rank}] Waiting for other workers... ({len(sync_state.get('ready_workers', []))}/{world_size} ready)")
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time.sleep(2)
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except Exception as e:
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print(f"[{rank}] Error during sync: {e}")
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time.sleep(2)
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else:
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raise RuntimeError(f"[{rank}] Timeout waiting for all workers to be ready!")
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# Small delay to ensure all workers see the ready state
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time.sleep(3)
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# STEP 3: Configure distributed environment
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os.environ['MASTER_ADDR'] = MASTER_ADDR
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os.environ['MASTER_PORT'] = MASTER_PORT
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os.environ['WORLD_SIZE'] = str(world_size)
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os.environ['RANK'] = str(rank)
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os.environ['NCCL_SOCKET_IFNAME'] = 'eth0'
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os.environ['NCCL_DEBUG'] = 'INFO'
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os.environ['NCCL_TIMEOUT'] = str(NCCL_TIMEOUT)
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os.environ['NCCL_BLOCKING_WAIT'] = '1'
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print(f"[{rank}] Environment configured:")
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print(f" MASTER_ADDR: {MASTER_ADDR}")
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print(f" MASTER_PORT: {MASTER_PORT}")
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print(f" RANK: {rank}")
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print(f" WORLD_SIZE: {world_size}")
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# STEP 4: Initialize process group
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print(f"[{rank}] Initializing process group (backend=nccl)...")
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try:
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dist.init_process_group(
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backend="nccl",
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init_method="env://",
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timeout=timedelta(minutes=10),
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rank=rank,
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world_size=world_size
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)
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print(f"[{rank}] ✓ Successfully joined distributed group!")
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print(f" Process group size: {dist.get_world_size()}")
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print(f" My rank: {dist.get_rank()}")
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except Exception as e:
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print(f"[{rank}] ✗ Failed to initialize process group!")
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print(f" Error: {str(e)}")
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raise
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# STEP 5: Setup GPU
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device = torch.device("cuda:0")
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torch.cuda.set_device(device)
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print(f"[{rank}] GPU Configuration:")
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print(f" Device: {torch.cuda.get_device_name(0)}")
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print(f" Memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
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# STEP 6: Define model
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model = nn.Sequential(
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nn.Linear(10, 128),
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nn.ReLU(),
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nn.Linear(128, 128),
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nn.ReLU(),
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nn.Linear(128, 10)
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).to(device)
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ddp_model = nn.parallel.DistributedDataParallel(
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model,
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device_ids=[0],
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output_device=0
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)
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criterion = nn.MSELoss()
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optimizer = optim.SGD(ddp_model.parameters(), lr=0.001, momentum=0.9)
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print(f"[{rank}] Model initialized with {sum(p.numel() for p in model.parameters())} parameters")
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# STEP 7: Training loop
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print(f"\n[{rank}] {'='*60}")
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print(f"[{rank}] Starting Training Loop")
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print(f"[{rank}] {'='*60}")
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num_epochs = 10
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batch_size = 32
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num_batches = 5
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for epoch in range(num_epochs):
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ddp_model.train()
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epoch_loss = 0.0
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for batch_idx in range(num_batches):
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torch.manual_seed(epoch * num_batches + batch_idx)
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inputs = torch.randn(batch_size, 10).to(device)
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labels = torch.randn(batch_size, 10).to(device)
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optimizer.zero_grad()
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outputs = ddp_model(inputs)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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epoch_loss += loss.item()
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avg_loss = epoch_loss / num_batches
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# Synchronize loss across ranks
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loss_tensor = torch.tensor([avg_loss]).to(device)
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dist.all_reduce(loss_tensor, op=dist.ReduceOp.AVG)
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global_avg_loss = loss_tensor.item()
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if rank == 0:
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print(f"[{rank}] Epoch {epoch+1}/{num_epochs} | Global Avg Loss: {global_avg_loss:.6f}")
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print(f"\n[{rank}] {'='*60}")
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print(f"[{rank}] Training Complete!")
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print(f"[{rank}] {'='*60}")
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# STEP 8: Cleanup
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dist.destroy_process_group()
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print(f"[{rank}] Process group destroyed. Finished at {datetime.now()}")
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return {
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'rank': rank,
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'final_loss': global_avg_loss if rank == 0 else avg_loss,
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'epochs_completed': num_epochs,
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'status': 'success'
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}
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def cleanup_sync_state_func():
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"""Clean up synchronization state"""
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try:
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Variable.delete('ddp_sync_state')
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print("Sync state cleaned up")
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except:
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pass
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return True
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def training_summary_func(**context):
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"""Aggregate and display training results"""
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ti = context['ti']
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print(f"\n{'='*60}")
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print(f"DISTRIBUTED TRAINING SUMMARY")
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print(f"{'='*60}")
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for rank in range(WORLD_SIZE):
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try:
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result = ti.xcom_pull(task_ids=f"train_rank_{rank}")
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if result and result.get('status') == 'success':
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print(f"Rank {result['rank']}: ✓ Completed {result['epochs_completed']} epochs")
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print(f" Final loss: {result['final_loss']:.6f}")
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except Exception as e:
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print(f"Warning: Could not get result for rank {rank}: {e}")
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print(f"\n✓ Training completed!")
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return {'status': 'success', 'workers': WORLD_SIZE}
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# -----------------------
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# DAG
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# -----------------------
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with DAG(
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dag_id="pytorch_ddp_airflow_fixed_production",
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start_date=datetime(2024, 1, 1),
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schedule=None,
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catchup=False,
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dag_id='pytorch_distributed_training_ddp_production',
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default_args=default_args,
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tags=["ddp", "pytorch", "gpu"],
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schedule=None,
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start_date=pendulum.today('UTC').add(days=-1),
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catchup=False,
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tags=['gpu', 'ml', 'distributed'],
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max_active_runs=1,
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max_active_tasks=10,
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) as dag:
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prepare_training_task = PythonOperator(
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task_id="prepare_training",
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python_callable=prepare_training,
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queue="gpu", # ✅ FIX #1
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# Preparation task
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prep = PythonOperator(
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task_id='prepare_training',
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python_callable=prepare_training_func,
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queue='gpu'
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)
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train_ddp_task = BashOperator(
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task_id="train_ddp",
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bash_command="""
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set -e
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# Training tasks - one per rank
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training_tasks = []
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for i in range(WORLD_SIZE):
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task = PythonOperator(
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task_id=f'train_rank_{i}',
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python_callable=run_training_node_func,
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op_kwargs={'rank': i, 'world_size': WORLD_SIZE},
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queue='gpu',
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)
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training_tasks.append(task)
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export NCCL_DEBUG=INFO
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export NCCL_ASYNC_ERROR_HANDLING=1
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export NCCL_IB_DISABLE=1
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echo "Starting torchrun DDP training"
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torchrun \
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--nproc_per_node=2 \
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--master_port=29500 \
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/opt/airflow/dags/repo/train.py
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echo "Training finished"
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""",
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queue="gpu", # ✅ FIX #1
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# Cleanup task
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cleanup = PythonOperator(
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task_id='cleanup_sync_state',
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python_callable=cleanup_sync_state_func,
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queue='gpu',
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trigger_rule='all_done'
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)
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cleanup_task = PythonOperator(
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task_id="cleanup",
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python_callable=cleanup,
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queue="gpu", # ✅ FIX #1
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# Summary task
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summary = PythonOperator(
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task_id='training_summary',
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python_callable=training_summary_func,
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queue='gpu',
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trigger_rule='all_done'
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)
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# DAG FLOW
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prepare_training_task >> train_ddp_task >> cleanup_task
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# Set dependencies
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prep >> training_tasks >> cleanup >> summary
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