Configuration and Persistence

/**
 * Session configuration parameters
 */
class ConfigProto {
  /** Get device type to count mapping */
  Map<String, Integer> getDeviceCountMap();
  
  /** Get number of threads for intra-op parallelism */
  int getIntraOpParallelismThreads();
  
  /** Get number of threads for inter-op parallelism */
  int getInterOpParallelismThreads();
  
  /** Check if soft placement is enabled */
  boolean getAllowSoftPlacement();
  
  /** Check if device placement logging is enabled */
  boolean getLogDevicePlacement();
  
  /** Get GPU configuration options */
  GPUOptions getGpuOptions();
  
  /** Check if CUDA malloc is allowed to grow */
  boolean getAllowSoftPlacement();
  
  /** Get graph optimization options */
  GraphOptions getGraphOptions();
  
  /** Get operation timeout in milliseconds */
  long getOperationTimeoutInMs();
  
  /** Get RPC options for distributed execution */
  RPCOptions getRpcOptions();
  
  /** Get cluster configuration */
  ClusterDef getClusterDef();
  
  /** Check if XLA JIT compilation is enabled globally */
  boolean getIsolateSessionState();
  
  /** Create a new builder for constructing ConfigProto */
  static Builder newBuilder();
  
  /** Builder for constructing ConfigProto instances */
  static class Builder {
    Builder putDeviceCount(String key, int value);
    Builder setIntraOpParallelismThreads(int threads);
    Builder setInterOpParallelismThreads(int threads);
    Builder setAllowSoftPlacement(boolean allow);
    Builder setLogDevicePlacement(boolean log);
    Builder setGpuOptions(GPUOptions options);
    Builder setGraphOptions(GraphOptions options);
    Builder setOperationTimeoutInMs(long timeout);
    Builder setRpcOptions(RPCOptions options);
    Builder setClusterDef(ClusterDef cluster);
    ConfigProto build();
  }
}

import org.tensorflow.framework.*;

// Create a basic session configuration
ConfigProto config = ConfigProto.newBuilder()
    .setAllowSoftPlacement(true)
    .setLogDevicePlacement(false)
    .setIntraOpParallelismThreads(4)
    .setInterOpParallelismThreads(2)
    .putDeviceCount("GPU", 1)
    .putDeviceCount("CPU", 1)
    .build();

// Configure GPU options
ConfigProto gpuConfig = ConfigProto.newBuilder()
    .setGpuOptions(GPUOptions.newBuilder()
        .setAllowGrowth(true)
        .setPerProcessGpuMemoryFraction(0.8)
        .addVisibleDeviceList("0")  // Use only GPU 0
        .build())
    .build();

// Configure graph optimization
ConfigProto optimizedConfig = ConfigProto.newBuilder()
    .setGraphOptions(GraphOptions.newBuilder()
        .setEnableRecvScheduling(true)
        .setOptimizerOptions(OptimizerOptions.newBuilder()
            .setDoCommonSubexpressionElimination(true)
            .setDoConstantFolding(true)
            .setDoFunctionInlining(true)
            .build())
        .build())
    .build();

/**
 * GPU configuration options
 */
class GPUOptions {
  /** Get GPU memory fraction (0.0 to 1.0) */
  double getPerProcessGpuMemoryFraction();
  
  /** Check if GPU memory growth is allowed */
  boolean getAllowGrowth();
  
  /** Get GPU memory allocator type */
  String getAllocatorType();
  
  /** Get deferred deletion bytes */
  long getDeferredDeletionBytes();
  
  /** Get list of visible GPU device IDs */
  List<String> getVisibleDeviceListList();
  
  /** Get polling inactive delay in microseconds */
  int getPollingInactiveDelayMsecs();
  
  /** Check if force GPU compatible is enabled */
  boolean getForceGpuCompatible();
  
  /** Get experimental options */
  Experimental getExperimental();
  
  static class Experimental {
    List<String> getVirtualDevicesList();
    boolean getUseUnifiedMemory();
    int getNumDevToDevCopyStreams();
    String getCollectiveRingOrder();
    int getTimestampedAllocator();
    String getKernelTrackerMaxInterval();
    int getKernelTrackerMaxBytes();
    int getKernelTrackerMaxPending();
    boolean getInternalFragmentationFraction();
    boolean getUseMultiDeviceIterator();
  }
}

/**
 * Graph optimization configuration options
 */
class GraphOptions {
  /** Check if receiver scheduling is enabled */
  boolean getEnableRecvScheduling();
  
  /** Get optimizer options */
  OptimizerOptions getOptimizerOptions();
  
  /** Get build cost model */
  long getBuildCostModel();
  
  /** Get inference time */
  long getInferenceTime();
  
  /** Get placement period */
  int getPlacementPeriod();
  
  /** Check if timeline tracing is enabled */
  boolean getEnableTimeline();
  
  /** Get rewrite options */
  RewriterConfig getRewriteOptions();
  
  static Builder newBuilder();
  
  static class Builder {
    Builder setEnableRecvScheduling(boolean enable);
    Builder setOptimizerOptions(OptimizerOptions options);
    Builder setBuildCostModel(long model);
    GraphOptions build();
  }
}

/**
 * Optimization configuration for graphs
 */
class OptimizerOptions {
  /** Check if common subexpression elimination is enabled */
  boolean getDoCommonSubexpressionElimination();
  
  /** Check if constant folding is enabled */
  boolean getDoConstantFolding();
  
  /** Check if function inlining is enabled */
  boolean getDoFunctionInlining();
  
  /** Get optimization level */
  Level getOptLevel();
  
  /** Get global JIT level */
  GlobalJitLevel getGlobalJitLevel();
  
  static Builder newBuilder();
  
  static class Builder {
    Builder setDoCommonSubexpressionElimination(boolean enable);
    Builder setDoConstantFolding(boolean enable);
    Builder setDoFunctionInlining(boolean enable);
    Builder setOptLevel(Level level);
    OptimizerOptions build();
  }
  
  enum Level {
    L1, L0
  }
  
  enum GlobalJitLevel {
    DEFAULT,
    OFF,
    ON_1,
    ON_2
  }
}

/**
 * RPC configuration for distributed execution
 */
class RPCOptions {
  /** Check if RPC compression is used */
  boolean getUseRpcForInprocessMaster();
  
  /** Get compression algorithm */
  String getCompressionAlgorithm();
  
  /** Get compression level */
  int getCompressionLevel();
  
  /** Check if streaming RPC is enabled */
  boolean getStreamingRpc();
  
  static Builder newBuilder();
  
  static class Builder {
    Builder setUseRpcForInprocessMaster(boolean use);
    Builder setCompressionAlgorithm(String algorithm);
    Builder setCompressionLevel(int level);
    RPCOptions build();
  }
}

/**
 * Container for serializing complete models with metadata
 */
class MetaGraphDef {
  /** Get metadata about the graph */
  MetaInfoDef getMetaInfoDef();
  
  /** Get the computation graph */
  GraphDef getGraphDef();
  
  /** Get saver configuration */
  SaverDef getSaverDef();
  
  /** Get collections mapping */
  Map<String, CollectionDef> getCollectionDefMap();
  
  /** Get input/output signatures */
  Map<String, SignatureDef> getSignatureDefMap();
  
  /** Get asset file definitions */
  List<AssetFileDef> getAssetFileDefList();
  
  /** Get object graph definition */
  SavedObjectGraph getObjectGraphDef();
  
  /** Create a new builder for constructing MetaGraphDef */
  static Builder newBuilder();
  
  /** Builder for constructing MetaGraphDef instances */
  static class Builder {
    Builder setMetaInfoDef(MetaInfoDef metaInfo);
    Builder setGraphDef(GraphDef graphDef);
    Builder setSaverDef(SaverDef saverDef);
    Builder putCollectionDef(String key, CollectionDef value);
    Builder putSignatureDef(String key, SignatureDef value);
    Builder addAssetFileDef(AssetFileDef assetFile);
    Builder setObjectGraphDef(SavedObjectGraph objectGraph);
    MetaGraphDef build();
  }
}

import org.tensorflow.framework.*;

// First create a simple graph for demonstration
GraphDef trainedGraph = GraphDef.newBuilder()
    .addNode(NodeDef.newBuilder()
        .setName("input")
        .setOp("Placeholder")
        .putAttr("dtype", AttrValue.newBuilder().setType(DataType.DT_FLOAT).build()))
    .addNode(NodeDef.newBuilder()
        .setName("output")
        .setOp("Identity")
        .addInput("input"))
    .build();

// Create a MetaGraphDef for the trained model
MetaGraphDef metaGraph = MetaGraphDef.newBuilder()
    .setMetaInfoDef(MetaInfoDef.newBuilder()
        .setMetaGraphVersion("1.15.0")
        .addTags("serve")
        .setTensorflowVersion("1.15.0")
        .setTensorflowGitVersion("v1.15.0"))
    .setGraphDef(trainedGraph)
    .setSaverDef(SaverDef.newBuilder()
        .setFilenameTensorName("save/Const:0")
        .setSaveTensorName("save/Identity:0")
        .setRestoreOpName("save/restore_all")
        .setMaxToKeep(5)
        .setKeepCheckpointEveryNHours(10000.0f))
    .putSignatureDef("serving_default", SignatureDef.newBuilder()
        .setMethodName("tensorflow/serving/predict")
        .putInputs("input", TensorInfo.newBuilder()
            .setName("input:0")
            .setDtype(DataType.DT_FLOAT)
            .setTensorShape(TensorShapeProto.newBuilder()
                .addDim(TensorShapeProto.Dim.newBuilder().setSize(-1))
                .addDim(TensorShapeProto.Dim.newBuilder().setSize(784)))
            .build())
        .putOutputs("output", TensorInfo.newBuilder()
            .setName("output:0")
            .setDtype(DataType.DT_FLOAT)
            .setTensorShape(TensorShapeProto.newBuilder()
                .addDim(TensorShapeProto.Dim.newBuilder().setSize(-1))
                .addDim(TensorShapeProto.Dim.newBuilder().setSize(10)))
            .build())
        .build())
    .build();

/**
 * High-level serialization format for TensorFlow models
 */
class SavedModel {
  /** Get schema version */
  long getSavedModelSchemaVersion();
  
  /** Get MetaGraphs */
  List<MetaGraphDef> getMetaGraphsList();
  
  /** Create a new builder for constructing SavedModel */
  static Builder newBuilder();
  
  /** Builder for constructing SavedModel instances */
  static class Builder {
    Builder setSavedModelSchemaVersion(long version);
    Builder addMetaGraphs(MetaGraphDef metaGraph);
    Builder addAllMetaGraphs(Iterable<MetaGraphDef> metaGraphs);
    SavedModel build();
  }
}

/**
 * Model input/output signature for serving
 */
class SignatureDef {
  /** Get signature inputs */
  Map<String, TensorInfo> getInputsMap();
  
  /** Get signature outputs */
  Map<String, TensorInfo> getOutputsMap();
  
  /** Get method name (e.g., 'tensorflow/serving/predict') */
  String getMethodName();
  
  /** Create a new builder for constructing SignatureDef */
  static Builder newBuilder();
  
  /** Builder for constructing SignatureDef instances */
  static class Builder {
    Builder putInputs(String key, TensorInfo value);
    Builder putOutputs(String key, TensorInfo value);
    Builder setMethodName(String methodName);
    SignatureDef build();
  }
}

/**
 * Information about tensors in model signatures
 */
class TensorInfo {
  /** Get tensor name */
  String getName();
  
  /** Get coordinate operation name (COO format) */
  CooSparse getCooSparse();
  
  /** Get composite tensor information */
  CompositeTensor getCompositeTensor();
  
  /** Get tensor data type */
  DataType getDtype();
  
  /** Get tensor shape */
  TensorShapeProto getTensorShape();
  
  /** Get which encoding is used */
  EncodingCase getEncodingCase();
  
  /** Create a new builder for constructing TensorInfo */
  static Builder newBuilder();
  
  /** Builder for constructing TensorInfo instances */
  static class Builder {
    Builder setName(String name);
    Builder setCooSparse(CooSparse cooSparse);
    Builder setCompositeTensor(CompositeTensor compositeTensor);
    Builder setDtype(DataType dtype);
    Builder setTensorShape(TensorShapeProto shape);
    TensorInfo build();
  }
  
  enum EncodingCase {
    NAME,
    COO_SPARSE,
    COMPOSITE_TENSOR,
    ENCODING_NOT_SET
  }
}

/**
 * Configuration for saving and restoring models
 */
class SaverDef {
  /** Get filename tensor name */
  String getFilenameTensorName();
  
  /** Get save tensor name */
  String getSaveTensorName();
  
  /** Get restore operation name */
  String getRestoreOpName();
  
  /** Get maximum number of checkpoints to keep */
  int getMaxToKeep();
  
  /** Get sharded save option */
  boolean getSharded();
  
  /** Get checkpoint save hours interval */
  float getKeepCheckpointEveryNHours();
  
  /** Get checkpoint version */
  CheckpointFormatVersion getVersion();
  
  enum CheckpointFormatVersion {
    LEGACY,
    V1,
    V2
  }
}

import org.tensorflow.framework.*;

// Create a serving signature for image classification
SignatureDef servingSignature = SignatureDef.newBuilder()
    .setMethodName("tensorflow/serving/predict")
    .putInputs("image", TensorInfo.newBuilder()
        .setName("input_image:0")
        .setDtype(DataType.DT_UINT8)
        .setTensorShape(TensorShapeProto.newBuilder()
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(-1))  // batch
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(224)) // height
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(224)) // width
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(3)))  // channels
        .build())
    .putOutputs("predictions", TensorInfo.newBuilder()
        .setName("output_predictions:0")
        .setDtype(DataType.DT_FLOAT)
        .setTensorShape(TensorShapeProto.newBuilder()
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(-1))    // batch
            .addDim(TensorShapeProto.Dim.newBuilder().setSize(1000))) // classes
        .build())
    .build();

// Create saver configuration
SaverDef saver = SaverDef.newBuilder()
    .setFilenameTensorName("save/Const:0")
    .setSaveTensorName("save/Identity:0")
    .setRestoreOpName("save/restore_all")
    .setMaxToKeep(10)
    .setSharded(true)
    .setKeepCheckpointEveryNHours(1.0f)
    .setVersion(SaverDef.CheckpointFormatVersion.V2)
    .build();