rithwik-db commented on code in PR #39267:
URL: https://github.com/apache/spark/pull/39267#discussion_r1069064636
##########
python/pyspark/ml/torch/distributor.py:
##########
@@ -428,6 +432,84 @@ def _run_local_training(
return output
+ def _get_spark_task_program(
+ self, framework_wrapper_fn: Optional[Callable], train_fn:
Union[Callable, str], *args: Any
+ ) -> Callable:
+ num_processes = self.num_processes
+ num_tasks = self.num_tasks
+ use_gpu = self.use_gpu
+ input_params = self.input_params
+
+ # Spark task program
+ def wrapped_train_fn(_): # type: ignore[no-untyped-def]
+ import os
+ from pyspark import BarrierTaskContext
+
+ CUDA_VISIBLE_DEVICES = "CUDA_VISIBLE_DEVICES"
+
+ # The idea of setting the random port to 0 doesn't seem to work?
+ def get_free_port(address: str) -> int:
+ import socket
+ import random
+
+ while True:
+ port = random.randint(32768, 61000)
+ sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+ if not (sock.connect_ex((address, port)) == 0):
+ return port
+
+ def set_torch_config(context: "BarrierTaskContext") -> None:
+ addrs = [e.address.split(":")[0] for e in
context.getTaskInfos()]
+
+ os.environ["MASTER_ADDR"] = str(addrs[0])
+ os.environ["MASTER_PORT"] = str(get_free_port(addrs[0]))
+ os.environ["WORLD_SIZE"] = str(num_processes)
+ os.environ["NODE_RANK"] = str(context.partitionId())
+ os.environ["RANK"] = str(context.partitionId())
+
+ def set_gpus(context: "BarrierTaskContext") -> None:
+ gpus_owned = get_gpus_owned(context)
+
+ my_num_gpus = (num_processes // num_tasks) + (
+ context.partitionId() < (num_processes % num_tasks)
+ )
+ gpu_addresses = [str(e) for e in random.sample(gpus_owned,
my_num_gpus)]
+ os.environ[CUDA_VISIBLE_DEVICES] = ",".join(gpu_addresses)
+
+ context = BarrierTaskContext.get()
+
+ if use_gpu:
+ set_gpus(context)
+ else:
+ os.environ[CUDA_VISIBLE_DEVICES] = ""
Review Comment:
PyTorch Lightning will raise a `MisconfigurationException: No supported gpu
backend found!`. This is what we expect to see if the user sets `use_gpu=False`
and calls `pl.Trainer(accelerator="gpu")` My understanding is that if a user
runs this code on a local cluster with GPUs on each node without
"os.environ[CUDA_VISIBLE_DEVICES] = ""`, then the task will be assigning the
task a GPU even when `use_gpu=True`.
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