High memory demands with large samples

[vladimirkovacevic]

I'm trying to process a sample with 90 000 observations and it gets killed (deficit of memory) during the execution of l_search function. The machine I'm using has 36 CPUs and 72 GB of RAM.
Do you maybe have any suggestions on how to optimize it? Would it help not to use "easy mode"?

def detect_spatial_domains_ez_mode(adata, img, x_array, y_array, x_pixel, y_pixel, n_clusters, histology=True, s=1, b=49, p=0.5, r_seed=100, t_seed=100, n_seed=100):
	adj=calculate_adj_matrix(x=x_pixel,y=y_pixel, x_pixel=x_pixel, y_pixel=y_pixel, image=img, beta=b, alpha=s, histology=histology)
	prefilter_genes(adata,min_cells=3) # avoiding all genes are zeros
	prefilter_specialgenes(adata)
	sc.pp.normalize_per_cell(adata)
	sc.pp.log1p(adata)
	l=search_l(p, adj, start=0.01, end=1000, tol=0.01, max_run=100)
	res=search_res(adata, adj, l, n_clusters, start=0.7, step=0.1, tol=5e-3, lr=0.05, max_epochs=20, r_seed=r_seed, t_seed=t_seed, n_seed=n_seed)
	clf=SpaGCN()
	clf.set_l(l)
	random.seed(r_seed)
	torch.manual_seed(t_seed)
	np.random.seed(n_seed)
	clf.train(adata,adj,init_spa=True,init="louvain",res=res, tol=5e-3, lr=0.05, max_epochs=200)
	y_pred, prob=clf.predict()
	return y_pred