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Upon receiving the reduce task information, the reducer goes through each mapper in the list returned by the VMR server in order.
If the reducer meets all the hardware and availability requirements, the scheduler replies with a reduce task that fits the request.
Once it has ascertained that the reducer meets all the hardware and availability requirements, the server replies with a reduce task that fits the request.
mapreduce.reduce.memory.mb 2880 The amount of memory for each reduce task.
mapreduce.reduce.java.opts -Xmx2304m Maximum JVM heap memory for reduce task.
On the other hand, VMR-30R3 uses 3 replicas for each reduce task.
It keeps tasks alive during the whole iterative process to reduce task initialization overhead.
Each map and reduce task has at least 2 replicas, running on different clients.
Each reduce task is usually responsible for processing the intermediate data associated with many intermediate keys.
In the Reduce phase, each reduce task runs the user-provided function.
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The model intends to reduce task-specific feature engineering and to generalize well to new data.
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