Am 01.02.21 um 12:32 schrieb James Ruffle:
Dear Graph-Tool community,
I am trying to construct a graph with a large number of edges, using an np file as an edge_list. There are 125760 vertices and an edge list of length (7907725920, 2).
In order to use the npy edge_list file, I have needed to load the edge list as a readable memmap because, at a size of 126Gb, it is far too large to load into memory. But, when calling add_edge_list to this memmap, I think it is still being loaded into memory as the RAM will fill and the python session crash. I suppose the alternative is that the graph object becomes too large to hold into memory, but with previous large graphs I did not find this to be the problem. Does anybody have a solution to this issue?
I'm not sure what kind of solution you are expecting. Do you want Graph.add_edge_list() *not* to load the edges into memory?
Lastly, after I find a means to add this number of edges, I need to assign weights to the edges, again from a memmap file due to its size, which gives me the same problem. Any advice?
*Sample code:*
#prime the graph with the number of vertices g = Graph(directed=False) g.add_vertex(125760)
#load the edge list as memmap and add it idx_indi_mmap = np.load('idx_indi.npy', mmap_mode='r’) idx_indi_mmap.shape #(7907725920, 2)
g.add_edge_list(idx_indi_mmap) #script will crash at this point from filling the RAM
#Then want to add weights by taking the indices from another memmap object node_matrix = np.load('node_matrix.npy', mmap_mode='r’) node_matrix.shape #(125760, 125760) weights = node_matrix[idx_indi_mmap] ew = g("double") ew.a = weights g.ep[‘weight'] = ew
You might save some intermediary memory by loading the weights in one go with Graph.add_edge_list() (see the 'eprops' parameter), which requires a numpy array with three values, (source, target, weight). However, if you do not have enough RAM to hold the entire graph + weights into memory, this is also not going to work. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>