Hi Liam, I would suggest start out by taking a look at the gRPC quickstart for Python at, https://grpc.io/docs/quickstart/python.html and then modifying that example to do what you would like. The Flask server would launch the separate process using multiprocessing. The model process would create a gRPC service endpoint. The Flask server would wait for the model process to start and then establish a gRPC connection as a client to the gRPC service endpoint of the model process. The gRPC service of the model process would have methods, such as trainModel or getModelStatus, … When an http request occurs on the Flask http server, the server would then invoke the gRPC methods in the model process. I hope that helps. Regards --Roland From: Liam Geron <liam@chatdesk.com> Date: Thursday, December 20, 2018 at 9:53 AM To: Roland Hochmuth <rhochmuth@alteryx.com> Cc: Scikit-learn mailing list <scikit-learn@python.org> Subject: Re: [scikit-learn] How to keep a model running in memory? Hi Roland, Thanks for the suggestion! I'll certainly look into gRPC or similar frameworks. Currently we have multiprocessing, but it's not used to that same extent. How would the second process have a sort of "listener" to respond to incoming requests if it is running persistently? Thanks so much for the help. Best, Liam On Thu, Dec 20, 2018 at 11:12 AM Roland Hochmuth <rhochmuth@alteryx.com<mailto:rhochmuth@alteryx.com>> wrote: Hi Liam, Not sure I have the complete context for what you are trying to do, but have you considered using Python multiprocessing to start a separate process? The lifecycle of that process could start when the Flask server starts-up or on the first request. The separate process would load and run the model. Depending on what you would like to do, some form of IPC mechanism, such as gRPC could be used to control or get updates from the model process. Regards --Roland From: scikit-learn <scikit-learn-bounces+rhochmuth=alteryx.com@python.org<mailto:alteryx.com@python.org>> on behalf of Aneto <aneto@chatdesk.com<mailto:aneto@chatdesk.com>> Reply-To: Scikit-learn mailing list <scikit-learn@python.org<mailto:scikit-learn@python.org>> Date: Thursday, December 20, 2018 at 8:21 AM To: "scikit-learn@python.org<mailto:scikit-learn@python.org>" <scikit-learn@python.org<mailto:scikit-learn@python.org>> Cc: Liam Geron <liam@chatdesk.com<mailto:liam@chatdesk.com>> Subject: [scikit-learn] How to keep a model running in memory? Hi scikit learn community, We currently use scikit-learn for a model that generates predictions on a server endpoint. We would like to keep the model running in memory instead of having to re-load the model for every new request that comes in to the server. Can you please point us in the right direction for this? Any tutorials or examples. In case it's helpful, we use Flask for our web server. Thank you! Aneto