问题
This is a follow up question to a potential answer to one of my previous questions on using Dask computed to access one element in a large array .
Why does using Dask compute cause the execution to hang below? Here's the working code snippet:
#Suppose you created a scheduler at the ip address of 111.111.11.11:8786
from dask.distributed import Client
import dask.array as da
# client1
client1 = Client("111.111.11.11:8786")
x = da.ones(10000000, chunks=(100000,)) # 1e7 size array cut into 1e5 size chunks
x = x.persist()
client1.publish_dataset(x=x)
# client2
client2 = Client("111.111.11.11:8786")
x = client2.get_dataset('x') #get the lazy collection x
result = x[0].compute() #code execution hangs here
print(result)
回答1:
persist behaves differently, depending on whether you have a distributed client active or not. In your case, you call it before making any client, with the result that the whole of the data is packed into the graph description. This behaviour is OK on the threaded scheduler, where memory is shared between workers, but when you publish, you are sending the whole thing to the scheduler, and apparently it is choking.
If you make client1 first, you will notice that persist happens very quickly (the scheduler is only getting pointers to the data in this case), and the publish-fetch cycle will work as expected.
来源：https://stackoverflow.com/questions/45468673/using-dask-compute-causes-execution-to-hang