It's been two years and there still doesn't seem to be a way to have a locally trained neural network model evaluated in the cloud. Is that correct?

Here's some simple sample code, everything but CloudDeploy is straight from the docs:

net = NetChain[{LinearLayer[], LogisticSigmoid}];
trained = NetTrain[net, {1->False, 2->False, 3->True, 4->True}]

trained[3.5] (* True *)

CloudDeploy[FormPage[{"n" -> "Number"}, trained[#n] &], Permissions -> "Public"] // SystemOpen

When you run it, and put in 3.5 into the form, it returns an errorCode "no-wolfram-engine-response", statusCode 503, and errorDetails "Unable to evaluate".

1) Am I doing something wrong? 2) Is there an ETA for when it will be possible? 3) Is there a workaround?

Thanks

Hi Philip,

The ClassifierFunction already has the minimal amount of data needed for prediction. The problem is that some models can be very large (e.g. NearestNeighbors or RandomForest).

The first thing you should do is to set the option PerformanceGoal -> "Memory" at training time. Then you can play with the Method option to see if you can find a model that corresponds to your need.

Thanks, Etienne

Here is a short related SE discussion from a year or two ago:

How can I export my learned ClassiferFunction and PredictorFunction ?

I am totally happy with exporting something to the cloud API, I just don't want it to be multiple gigabytes, for a presumably much shorter amount of code that is the model per se.

The classifierfunction (in an efficient form) should be much smaller than the input data. The entire idea of the classifier is to make a 'model' of it, and a model is by definition simpler than the original... It could be that Mathematica stores the original data (the input data) also in the classifierfunction ;-)