在模块化python代码库中使用Dask LocalCluster（）

我正在尝试使用Dask分布式系统，使用一台机器的所有内核并行运行代码

考虑一个示例python数据管道，其文件夹结构如下

sample_dask_program
├── main.py
├── parallel_process_1.py
├── parallel_process_2.py
├── process_1.py
├── process_2.py
└── process_3.py

main.py是入口点，它在管道顺序运行时执行

例如：

parallel_process_1.py和parallel_process_2.py是创建Client（）并用于实现并行性的模块

with Client() as client:
            # list to store futures after they are submitted
            futures = []

            for item in items:
                future = client.submit(
                    ...
                )
                futures.append(future)

            results = client.gather(futures)

process_1.py、process_2.py和process_3.py是执行简单计算的模块，不需要使用所有CPU内核并行运行

回溯：

  File "/sm/src/calculation/parallel.py", line 140, in convert_qty_to_float
    results = client.gather(futures)
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/client.py", line 1894, in gather
    asynchronous=asynchronous,
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/client.py", line 778, in sync
    self.loop, func, *args, callback_timeout=callback_timeout, **kwargs
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/utils.py", line 348, in sync
    raise exc.with_traceback(tb)
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/utils.py", line 332, in f
    result[0] = yield future
  File "/home/iouser/.local/lib/python3.7/site-packages/tornado/gen.py", line 735, in run
    value = future.result()
concurrent.futures._base.CancelledError

这是工作人员引发的错误：

distributed.worker - ERROR - failed during get data with tcp://127.0.0.1:33901 -> tcp://127.0.0.1:38821
Traceback (most recent call last):
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/comm/tcp.py", line 248, in write
    future = stream.write(frame)
  File "/home/iouser/.local/lib/python3.7/site-packages/tornado/iostream.py", line 546, in write
    self._check_closed()
  File "/home/iouser/.local/lib/python3.7/site-packages/tornado/iostream.py", line 1035, in _check_closed
    raise StreamClosedError(real_error=self.error)
tornado.iostream.StreamClosedError: Stream is closed
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/worker.py", line 1248, in get_data
    compressed = await comm.write(msg, serializers=serializers)
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/comm/tcp.py", line 255, in write
    convert_stream_closed_error(self, e)
  File "/home/iouser/.local/lib/python3.7/site-packages/distributed/comm/tcp.py", line 121, in convert_stream_closed_error
    raise CommClosedError("in %s: %s: %s" % (obj, exc.__class__.__name__, exc))
distributed.comm.core.CommClosedError: in <closed TCP>: BrokenPipeError: [Errno 32] Broken pipe

但是，仅使用线程创建LocalCluster会将执行速度降低2-3倍

---

那么，找到适合该程序的适当数量的进程/线程是问题的解决方案吗？

更常见的做法是创建一个Dask客户机，然后在其上运行许多工作负载

with Client() as client:
    stage_one(client)
    stage_two(client)

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话虽如此，你所做的应该是好的。如果你能用最少的例子重现错误，那将很有用（但没有期望）。

可能会有帮助。谢谢@mdurant，这个页面帮助我得到了。我正在做的计算涉及NumPy/熊猫。当进程数相对较高时，似乎会出现错误。

with Client() as client:
    stage_one(client)
    stage_two(client)