backed="r" is leaking memory

[agemagician]

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Hello,

Using the lazy loader, I am trying to load a big h5ad file that can't fit into memory. It works as expected and doesn't load the whole file into memory at the start.
However, if I started to read the data row by row, the memory utilization increased to the same file size.

My expectation is that the lazy loader should read each row and free its associated memory after it is deleted, which is not the case.

How can we solve this issue?

Code:

import scanpy as sc
import sys
from tqdm import tqdm

file_name = "file.h5ad"

adata_r = sc.read_h5ad(file_name, backed="r")

for idx in tqdm(range(adata_r.X.shape[0])):
    row = adata_r.X[idx].toarray()
    del row

100%|▌| 800000/800000 [20:00<20:00, 700.60it/s]

Versions

-----
anndata             0.10.8
scanpy              1.9.8
session_info        1.0.0
tqdm                4.66.5
-----
IPython             8.21.0
jupyter_client      8.6.0
jupyter_core        5.7.1
jupyterlab          2.3.2
notebook            6.4.10
-----
Python 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0]
Linux-5.10.219-208.866.amzn2.x86_64-x86_64-with-glibc2.35
-----
Session information updated at 2024-08-14 12:33

[ilan-gold]

Hello @agemagician, we will need a little bit more to go on here. Your example did not produce the same results for me on a large dataset I had locally. Could you share your dataset? Could it be CSC?

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