We at the UMN NLP/IE Lab have developed NLP-ADAPT-kube to scale out 4-UIMA NLP annotators using Kubernetes/UIMA-AS, including cTAKES, CLAMP, MetaMap (using the UIMA wrapper), and our own homegrown BioMedICUS. Our project is here: https://github.com/nlpie/nlp-adapt-kube
There are 2-versions: One for CPM, which includes QuickUMLS; and the other for UIMA-AS. The AS versions are under the docker folder and the argo-k8s folder, and use the 4-engines mentioned above. There is a project Wiki (but it is slightly out-of-date). We are in the process of working non-UIMA engines (like QuickUMLS and our new version of BioMedICUS) into the AS workflow (we're using AMQ for message queuing). We're currently running cTAKES using Kubernetes hpa with 6-backends and 2-clients across 3-compute nodes getting very decent throughput (~150 docs/second). We could definitely scale it up even further. For comparison how well this scales, we were running 64-MetaMap backends with 16-clients and getting ~40 docs/second for very large clinical documents (which for MetaMap is very decent). This was across 5-compute nodes. If you're interested, we can assist in implementation. The client does require some customizations based on the backend database you're using: https://github.com/nlpie/nlp-adapt-kube/tree/master/docker/as/client, but that is pretty straightforward. Best! Greg-- On Tue, Nov 17, 2020 at 10:47 AM John Doe <[email protected]> wrote: > Hello, > > I'm new to cTAKES and was wondering what the options are for scaling out > the default clinical pipeline. I'm running it on a large number of clinical > notes using runClinicalPipeline.bat and specifying the input directory with > the notes. What are the best options for doing this in a more scalable way? > For example, can I parallelize it with UIMA-AS? Or should I manually use > multiple command prompts to run the clinical pipeline on a different set of > clinical notes in parallel? I'm not sure if there is any build-in solution > or community resource which uses EMR/Spark or some other method to achieve > this. > > Thank you for your help. > -- Greg M. Silverman Senior Systems Developer NLP/IE <https://healthinformatics.umn.edu/research/nlpie-group> Department of Surgery University of Minnesota [email protected]
