use "session.runner.run " to predict with tensors，after many times, the memory grow higher and higher then oom

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System information

• Have I written custom code (as opposed to using a stock example script provided in TensorFlow):
• OS Platform and Distribution (e.g., Linux Ubuntu 16.04 x86_64):Linux x86_64)
• TensorFlow installed from (source or binary):
• TensorFlow version (use command below):
• org.tensorflow tensorflow-core-api 0.4.2 org.tensorflow tensorflow-core-native 1.0.0-rc.2 linux-x86_64
• Java version (i.e., the output of java -version): java 8
• Java command line flags (e.g., GC parameters):
• Python version (if transferring a model trained in Python):
• Bazel version (if compiling from source):
• GCC/Compiler version (if compiling from source):
• CUDA/cuDNN version:
• GPU model and memory:

Describe the current behavior
we use "session.runner.run " to predict with tensors online，After hours of requests, the instance's memory kept increasing and eventually resulted in an out-of-memory (OOM) (or core dumped )error,

here is my behavior
At the start of the service, we load the session using the loadTensorflowModel method and persist it in memory. During runtime, a complete request process involves: 1. Feeding the Tensor using session.runner.feed, 2. Fetching the results using session.runner.fetch, 3. Obtaining the model's scoring results using the session.runner.run method, 4. Finally, closing both the input tensors and output tensors to complete the request and return the response.

Describe the expected behavior
I desire to keep the memory stable, maintaining it around 65%, rather than continuously increasing over time.

Code to reproduce the issue

package org.example
import org.tensorflow.SavedModelBundle
import org.tensorflow.ndarray.Shape
import org.tensorflow.ndarray.impl.buffer.raw.RawDataBufferFactory
import org.tensorflow.ndarray.buffer.FloatDataBuffer
import org.tensorflow.proto.framework.{ConfigProto, RunOptions}
import org.tensorflow.types.TFloat32

import scala.collection.JavaConversions.iterableAsScalaIterable
import scala.collection.JavaConverters.{asScalaBufferConverter, iterableAsScalaIterableConverter}
import scala.util.Random

object test {
  def main(args: Array[String]): Unit = {
    valpath = "/xxxxx/tensorflow_test"
    val kernel = loadTensorflowModel("/xxxxx/tensorflow_test")
    val json = 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    val data = JacksonUtil.decode[Map[String,Seq[String]]](json)
    val runner = kernel.session().runner()
    val runTimes = (0 to 1000000).toSeq
    runTimes.par.foreach(num =>{
      val inputTensorList: java.util.ArrayList[TFloat32] = new java.util.ArrayList[TFloat32]()
      //第一步构建输入tensor
      data.map{
        case (tensorName, featureId) => {
          //默认每个inputTensor是一个一维数组
          val seq = featureId.map(p => Random.nextFloat()).toSeq
          val data: FloatDataBuffer = RawDataBufferFactory.create(seq.toArray,true)
          val shape: Shape = Shape.of(1,seq.size)

          val tensor = TFloat32.tensorOf(shape,data)
          runner.feed(tensorName,tensor)
          inputTensorList.add(tensor)
        }
      }

      //构建输出tensor
      for(i <- 0 until 2 ){
        runner.fetch("StatefulPartitionedCall",i)
      }

      //开始预测
      val output = runner.run().asScala

      val scores:Array[Float] = output.map(ten => {
        ten.asRawTensor().data().asFloats().getFloat(0)
      }).toArray
      inputTensorList.asScala.foreach(_.close())
      output.foreach(_.close())
      print(scores)
    })
  }

  def loadTensorflowModel(path:String):Session= {
    val config = ConfigProto.newBuilder
    .putDeviceCount("CPU",  Runtime.getRuntime.availableProcessors)
    .setInterOpParallelismThreads(8) // 设置操作之间的线程数
    .setIntraOpParallelismThreads(8) // 设置操作内的线程数
    .setOperationTimeoutInMs(500)
    .build

    val options = RunOptions.newBuilder
      .setTimeoutInMs(500)
      .build // 设置超时时间为 5 秒

    val modelBundle = SavedModelBundle
      .loader(s"$path")
      .withTags("serve")
      .withConfigProto(config)
      .withRunOptions(options)
      .load
    modelBundle.session()
  }

}

Other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

when i stop to request ,then the memory would gradually decrease until it returns to normal levels.

modelFile:after unzip， and load the path
test_model.zip

提供されている Scala の再現コードから始め、loadTensorflowModel と session.runner.feed、fetch、run のライフサイクルを追跡します。TensorFlow Java のバージョンと、入力テンソルおよび出力テンソルのクローズ動作を確認し、その後、メモリを測定しながらリクエストの繰り返しを分離します。再現コードでメモリが無制限に増加せず、OOM に到達しなくなれば完了です。