Comments (2)
In the latest version, it is now possible to use memmapping when using parallel execution using the temp_folder
variable on the Node2Vec
constructor. Just pass there a location with enough space and it is supposed to work
from node2vec.
When this bug is fixed in Python upstream https://bugs.python.org/issue17560 you may find this fixed without having to modify your code. However it may be the case that at such large sizes there are better ways for handling the shared state in the first place.
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Related Issues (20)
- How can node2vec use GPU HOT 1
- too slow and stuck for some reason HOT 9
- reproducability HOT 2
- failing simple clustering of synthetic networks HOT 2
- Error in model creation node2vec HOT 1
- Cannot import Node2Vec HOT 5
- A faster implement HOT 1
- Train vs Inference methods HOT 6
- [Question] about directed graph and weight HOT 2
- There may be a small problem with the walking length. HOT 1
- Interpretation of weights HOT 2
- model save and loading examples HOT 2
- Are there any ways to initialize node vector using pretrained embeddings? HOT 1
- Crash when edges are not initialised with a weight attribute HOT 8
- can same temp directory be used for different graphs in for loop training? HOT 4
- _precompute_probabilities parallel execution HOT 2
- import error HOT 3
- segmentation fault HOT 5
- Error installing gensim in poetry env on mac m1 HOT 3
- How to set parameters for a collection small disconnected graphs HOT 2
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from node2vec.