Memory error even with managing it - Question

Hi !

I am having trouble with dask memory management, I am trying to interpolate a large raster using dask, rioxarray and rasterio.


I open my GTiff file using rioxarray and I chunk it to get a dask array :

import rioxarray
import rasterio

ds = rioxarray.open_rasterio(filename="D:\\Documents\\MISSIONS\\DASK\\interpolation\\echant_much_more_bigger_30cm_pix_l93.tif", 
                             chunks=(1, 5000, 5000)).astype(rasterio.int8)

Then, I recover all my chunks thanks to this function :

chunks =

To interpolate, I use the rasterio.fill.fillnodata function so I have to create a mask array :

import numpy as np

def compute_chunk(chunk):
    return chunk.compute()

def create_mask(chunk):
    mask = np.where(chunk < 9, 0, chunk)
    return mask
masks = []
for chunk in chunks:
    computed = client.submit(compute_chunk, chunk)
    mask = client.submit(create_mask, computed)
    del computed
    del mask


When my masks array is created, my processing memory is around 8 GiB with about 2 GiB of unmanaged memory (1 old and 1 recent). So when I try to get the results of my mask (that is an array of futures) like this :

res = [future.result() for future in masks]

I get a MemoryError.

I did some researches and I tried to change my memory manging method but I think I’m doing something wrong… but I don’t know what. Did someone have an idea or an advice ?

Thanks you in advance ! :slight_smile:


@ClementAlba Welcome to Dask! Apologies for the delay in response.

I think we can optimize your code to use Dask more efficiently, which will help with memory management:

  • You need not use Delayed here. We can use Dask Array API directly becasue is a Dask Array (mixing collection isn’t generally recommended).
  • compute_chunk() isn’t necessary, you can directly do computed = chunks.compute() – not only are for-loops in general bottlenecks, but we don’t really need to compute the chunks at all. We can continue in a lazy fashion.
  • You can use map_blocks instead of for-loop + create_mask. Or, da.where(), Dask Array’s where function directly.

Reproducibe example:

import dask

import rioxarray
import rasterio

import dask.array as da
import numpy as np

from distributed import Client

client = Client()

ds = rioxarray.open_rasterio(filename="", 
                             chunks=(1, 5000, 5000)).astype(rasterio.int8)

chunks =

result = da.where(chunks < 9, 0, chunks)
1 Like

Hi @pavithraes, thanks for your response !

I wasn’t available for more than one month and I tried your solution when I was back. Unfortunately it not worked but I finally solve the problem.

After creating my masks I wan’t to interpolate my data and so as not to overflow my memory, each time a tile was interpoled, I wrote it in a directory (so I don’t keep any array in my memory). The idea is to keep all my functions lazy in my for-loop and after, do the computation :

# Cette fonction permet de créer le mask pour un chunk donné
def create_mask(chunk):
    mask = np.where(chunk < config.get("mask").get("limit"), 0, chunk)
    return mask

# Cette fonction permet d'interpoler un chunk donné. Elle retourne un tableau numpy
def interpolation(chunk, mask):
    return rasterio.fill.fillnodata(chunk, mask, config.get("interpolation").get("max_search_distance"))

# Cette fonction permet de créer un DataArray à partir d'un chunk. L'intérêt d'utiliser les DataArrays est que l'on peut facilement
# les convertir en raster
def create_data_array(chunk, coords):
    return xr.DataArray(
        dims=["band", "y", "x"],

# Cette fonction permet d'exporter un chunk au format .tif
def write_tile(tile, i):"directories").get("output") + "/" + str(i) + ".tif")
    for k in range(len(delayed_chunks)):
        # Calcul du mask
        mask = dask.delayed(create_mask)(delayed_chunks[k])
        # Interpolation du chunk
        interpoled = dask.delayed(interpolation)(delayed_chunks[k], mask)
        # Création du DataArray
        data_array = dask.delayed(create_data_array)(interpoled, coordinates[k])
        # Rangement dans le tableau
        tiles_to_write.append(dask.delayed(write_tile)(data_array, k))

By doing this strategy I don’t have memory error yet. I also manage my workers and my threads_per_worker when I create my Client.

If you wan’t to see my full code you can go to my GitHub repository : parallelisation_process/interpolation at main · ClementAlba/parallelisation_process · GitHub

Obviously, if you have advices or remarks on my usage of Dask, I’d be happy to read them !