Add low_memory option to FAQ
@@ -79,6 +79,19 @@ show the underlying detail that can otherwise be lost. We
highly recommend investing the time to learn datashader for
UMAP plot particularly for larger datasets.
+I ran out of memory. Help!
+--------------------------
+
+For some datasets the default options for approximate
+nearest neighbor search can result in excessive memory use.
+If your dataset is not especially large but you have found
+that UMAP runs out of memory when operating on it consider
+using the ``low_memory=True`` option, which will switch
+to a slower but less memory intensive approach to computing
+the approximate nearest neighbors. This may alleviate your
+issues.
+
+
Is there GPU or multicore-CPU support?
--------------------------------------
@@ -99,6 +112,10 @@ been deferred for now. If you have expertise in GPU
programming with Numba and would be interested in
adding GPU support we would welcome your contributions.
+There is a UMAP implementation for GPU available in
+the NVIDIA RAPIDS cuML library, so if you need GPU
+support that is currently the best palce to go.
+
Can I add a custom loss function?
---------------------------------
@@ -230,5 +247,10 @@ Successful use-cases
UMAP can be / has been Successfully applied to the following domains:
+- Single cell data visualization in biology;
+- Mapping malware based on behavioural data;
- Pre-processing phrase vectors for clustering;
- Pre-processing image embeddings (Inception) for clustering;
+
+and many more -- if you have a successful use-case please submit
+a pull request adding it to this list!
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