Session crashed after applying even AlignedUmap to higher dimension dataset

I would like to do dimension reduction on a higher dimension dataset using UMAP. Please find the dimension of the dataset -

X_train.shape(data) - (43874572,17)
y_train.shape(labels) - (43874572, )

The session got trashed when I apply AlignedUMAP dimensionality reduction- https://umap-learn.readthedocs.io/en/latest/aligned_umap_basic_usage.html

slices = [ordered_digits[12000* i:min(ordered_digits.shape[0], 12000* i + 50000)] for i in range(100)]
relation_dict = {i+150:i for i in range(400-150)
relation_dicts = [relation_dict.copy() for i in range(len(slices) - 1)]
%%time
aligned_mapper = umap.AlignedUMAP().fit(slices, relations=relation_dicts)
def axis_bounds(embedding):
    left, right = embedding.T[0].min(), embedding.T[0].max()
    bottom, top = embedding.T[1].min(), embedding.T[1].max()
    adj_h, adj_v = (right - left) * 0.1, (top - bottom) * 0.1
    return [left - adj_h, right + adj_h, bottom - adj_v, top + adj_v
fig, axs = plt.subplots(5,2, figsize=(10, 20))
ax_bound = axis_bounds(np.vstack(aligned_mapper.embeddings_))
for i, ax in enumerate(axs.flatten()):
    current_target = y_train[15000 * i:min(y_train.shape[0], 15000 * i + 40000)]
    ax.scatter(*aligned_mapper.embeddings_[i].T, s=2, c=current_target, cmap="Spectral")
    ax.axis(ax_bound)
    ax.set(xticks=[], yticks=[])
plt.tight_layout()

The error message I got -

Your session crashed after using all available RAM. If you are interested in access to high-RAM runtimes, you may want to check out google colab pro- 

How to reduce the dimensionality of higher dimension datasets using AlignedUMAP?

Start with the AlignedUMAP basic usage documentation linked in the issue and the provided AlignedUMAP().fit call. Reproduce the session crash with the stated dataset dimensions and inspect memory use across the slices. Done means establishing whether this is an implementation bug or a documented resource limitation, with a clear supported path or explanation.