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my test code with language scala ,this is one predict, we will predict 100 QPS for a docker
val config = ConfigProto.newBuilder
.putDeviceCount("CPU", Runtime.getRuntime.availableProcessors)
.setInterOpParallelismThreads(8)
.setIntraOpParallelismThreads(8)
.setOperationTimeoutInMs(3000)
.build
val options = RunOptions.newBuilder
.setTimeoutInMs(5000)
.build
val modelBundle = SavedModelBundle
.loader(s"$path")
.withTags("serve")
.withConfigProto(config.toByteArray)
.withRunOptions(options.toByteArray)
.load
val kernel = modelBundle.session
val data = Map("tensor1" -> Seq(0.1f,0.122f),……)
val runner = kernel.runner()
val inputTensorList: util.ArrayList[Tensor[java.lang.Float]] = new util.ArrayList[Tensor[java.lang.Float]]()
data.map{
case (tensorName, featureId) => {
val dataInput:FloatBuffer = FloatBuffer.allocate(featureId.size)
featureId.foreach(featureValue => {
dataInput.put(featureValue)
})
dataInput.asInstanceOf[Buffer].flip()
val tensorShape:Array[Long] = Array(1,featureId.size)
val tensor = Tensor.create(tensorShape,dataInput)
runner.feed(tensorName,tensor)
inputTensorList.add(tensor)
}
}
for(i <- 0 until 2 ){
runner.fetch("StatefulPartitionedCall",i)
}
val output = runner.run.asScala
val scores:Array[Float] = output.map(ten => {
val tensorData: Array[Array[Float]] = ten.copyTo(Array.ofDim[Float](ten.shape()(0).toInt, ten.shape()(1).toInt))
tensorData(0).head
}).toArray
inputTensorList.asScala.foreach(_.close())
output.foreach(_.close())
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