Comments (2)
Some additional observations...
(1) the above makes no use of netcdf files :
it just stores to an in-memory array.
But the effect is just the same
(2) the da.stack
usage (or at least, some combination of separate arrays) is essential to this problem
If in the above, you replace the source data with ...
lazydata_all = da.random.uniform(
0, 1,
size=(nt, nd),
chunks=(1, nd)
)
... then the problem does not occur.
(3) The result sample is using a threaded scheduler with 4 workers (CPUs) available.
We have confirmed that the same memory cost occurs with a distributed scheduler, but tracemalloc
cannot measure this as it is distributed across workers in separate processes. With large enough data, the effect can be seen on the system monitor.
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@dcherian sorry to personally disturb you, but have you maybe experienced a similar problem and can advise ?
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