Fix DDP master address for dynamic task scheduling
The fundamental issue was that MASTER_ADDR was hardcoded to airflow-worker-gpu-0, but rank 0 might run on any worker due to Celery's dynamic task scheduling. Now rank 0 dynamically stores its FQDN as the master address, and all other ranks connect to it. Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -76,11 +76,39 @@ def run_training_node_func(rank, world_size):
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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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# STEP 2.5: Rank 0 stores its address, others wait for it
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print(f"[{rank}] Determining master address...")
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my_fqdn = socket.getfqdn()
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print(f"[{rank}] My FQDN: {my_fqdn}")
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if rank == 0:
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# Rank 0 stores its address as the master
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sync_state = json.loads(Variable.get('ddp_sync_state', default_var='{}'))
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sync_state['master_addr'] = my_fqdn
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Variable.set('ddp_sync_state', json.dumps(sync_state))
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print(f"[{rank}] I am the master. Stored my address: {my_fqdn}")
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else:
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# Other ranks wait for rank 0 to store its address
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max_wait = 60
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start_wait = time.time()
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while time.time() - start_wait < max_wait:
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sync_state = json.loads(Variable.get('ddp_sync_state', default_var='{}'))
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if 'master_addr' in sync_state:
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master_addr = sync_state['master_addr']
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print(f"[{rank}] Master address retrieved: {master_addr}")
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break
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print(f"[{rank}] Waiting for master address...")
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time.sleep(1)
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else:
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raise RuntimeError(f"[{rank}] Timeout waiting for master address!")
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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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# Use dynamic master address from rank 0
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actual_master_addr = my_fqdn if rank == 0 else sync_state.get('master_addr', MASTER_ADDR)
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os.environ['MASTER_ADDR'] = actual_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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