Hello,

I am facing the same memory leak issue. I am using mpirun and dask-mpi on a slurm batch submission (see below). I am running through a time loop to perform some computations. After few iterations, the code blows up because out of memory issue. This does not happen if I execute the same code as a serial job. ``` from dask_mpi import initialize initialize()

from dask.distributed import Client client = Client()

main code goes here

ds = xr.open_mfdataset("*nc")

for i in range(0, len(ds.time)): ds1 = ds.isel(time=i) # perform some computations here

    ds1.close()

ds.close() ````

I have tried the following - explicit ds.close() calls on datasets - gc.collect() - client.cancel(vars)

None of the solutions worked for me. I have also tried increasing RAM but that didn't help either. I was wondering if anyone has found a work around this problem. @lumbric @shoyer @lkilcher

I am using dask 2022.2.0 dask-mpi 2021.11.0 xarray 0.21.1

Hey folks, I ran into a similar memory leak issue. In my case a had the following:

for num in range(100):
    ds = xr.open_dataset('data.{}.nc'.format(num)) # This data was compressed with zlib, not sure if that matters

    # do some stuff, but NOT assigning any data in ds to new variables

    del ds

For some reason (maybe having to do with the # do some stuff), ds wasn't actually getting cleared. I was able to fix the problem by manually triggering garbage collection (import gc, and gc.collect() after the del ds statement). Perhaps this will help others who end up here...

Using autoclose=True doesn't seem to make a difference. My test only uses 4 files anyway.

Thanks for the explanation of open_dataset() - that makes sense.

I've discovered that setting the environment variable MALLOC_MMAP_MAX_ to a reasonably small value can partially mitigate this memory fragmentation.

Performing 4 iterations over dataset slices of shape ~(5424, 5424) without this tweak was yielding >800MB of memory usage (an increase of ~400MB over the first iteration).

Setting MALLOC_MMAP_MAX_=40960 yielded ~410 MB of memory usage (an increase of only ~130MB over the first iteration).

This level of fragmentation is still offensive, but this does suggest the problem may lie deeper within the entire unix, glibc, Python, xarray, dask ecosystem.

I'm now wondering if this issue is in dask land, based on this issue: https://github.com/dask/dask/issues/3247

It has been suggested in other places to get around the memory accumulation by running each loop iteration in a forked process:

```python def worker(ds, k): print('accessing data') data = ds.datavar[k,:,:].values print('data acquired')

for k in range(ds.dims['t']): p = multiprocessing.Process(target=worker, args=(ds, k)) p.start() p.join() ``` But apparently one can't access dask-wrapped xarray datasets in subprocesses without a deadlock. I don't know enough about the internals to understand why.

Yes, I understand the garbage collection. The problem I'm struggling with here is that normally when working with arrays, maintaining only one reference to an array and replacing the data that reference points to within a loop does not result in memory accumulation because GC is triggered on the prior, now dereferenced array from the previous iteration.

Here, it seems that under the hood, references to arrays have been created other than my "data" variable that are not being dereferenced when I reassign to "data," so stuff is accumulating in memory.