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distributed_deadlock
infrastructure-distributed-deadlock-a-ray-worker-raises-an-exception-e-g-torch-cuda-se-6a76daf7
IFA Ray worker raises an exception (e.g., torch.cuda.set_device failure) before calling dist.init_process_group in a vLLM distributed inference setup; the main process blocks indefinitely on dist.init_process_group while the worker waits for ray.get, leading to deadlock.
THENTo prevent deadlock, use multithreading: one thread to call ray.wait() to monitor worker exceptions, and another thread to call dist.init_process_group(). Alternatively, design initialization to handle exceptions before any distributed synchronization point—ensure all workers successfully complete initialization tasks (e.g., CUDA device setting) before entering process group creation.
Tier 170%
distributed_deadlock
infrastructure-distributed-deadlock-when-a-ray-worker-raises-an-exception-before-calli-b3d60ffe
IFWhen a Ray worker raises an exception before calling init_process_group in distributed inference, the main process deadlocks because it waits for process group initialization while the worker is in an error state waiting for ray.get.
THENUse multithreading to concurrently wait for worker exceptions via ray.wait and initialize the process group. Alternatively, implement a timeout or kill all workers upon the first exception to prevent deadlock.
Tier 170%
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