Hey, i don't think that's the issue, foreach is called on 'results' which is
a DStream of floats, so naturally it passes RDDs to its function.
And either way, changing the code in the first mapper to comment out the map
reduce process on the RDD
Float f = 1.0f; //nnRdd.map(new FunctionNeuralNet,
Say you have a spark streaming setup such as
JavaReceiverInputDStream... rndLists = jssc.receiverStream(new
JavaRandomReceiver(...));
rndLists.map(new NeuralNetMapper(...))
.foreach(new JavaSyncBarrier(...));
Is there any way of ensuring that, say, a JavaRandomReceiver and
Hey guys, so the problem i'm trying to tackle is the following:
- I need a data source that emits messages at a certain frequency
- There are N neural nets that need to process each message individually
- The outputs from all neural nets are aggregated and only when all N
outputs for each message
