Package org.tensorflow.framework

Interface RunMetadataOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
RunMetadata, RunMetadata.Builder
public interface RunMetadataOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • hasStepStats

      boolean hasStepStats()
       Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
      .tensorflow.StepStats step_stats = 1;
      Returns:
      Whether the stepStats field is set.

    • getStepStats

      StepStats getStepStats()
       Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
      .tensorflow.StepStats step_stats = 1;
      Returns:
      The stepStats.

    • getStepStatsOrBuilder

      StepStatsOrBuilder getStepStatsOrBuilder()
       Statistics traced for this step. Populated if tracing is turned on via the
 "RunOptions" proto.
 EXPERIMENTAL: The format and set of events may change in future versions.
      .tensorflow.StepStats step_stats = 1;

    • hasCostGraph

      boolean hasCostGraph()
       The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;
      Returns:
      Whether the costGraph field is set.

    • getCostGraph

      CostGraphDef getCostGraph()
       The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;
      Returns:
      The costGraph.

    • getCostGraphOrBuilder

      CostGraphDefOrBuilder getCostGraphOrBuilder()
       The cost graph for the computation defined by the run call.
      .tensorflow.CostGraphDef cost_graph = 2;

    • getPartitionGraphsList

      List<GraphDef> getPartitionGraphsList()
       Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphs

      GraphDef getPartitionGraphs(int index)
       Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsCount

      int getPartitionGraphsCount()
       Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsOrBuilderList

      List<? extends GraphDefOrBuilder> getPartitionGraphsOrBuilderList()
       Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getPartitionGraphsOrBuilder

      GraphDefOrBuilder getPartitionGraphsOrBuilder(int index)
       Graphs of the partitions executed by executors.
      repeated .tensorflow.GraphDef partition_graphs = 3;

    • getFunctionGraphsList

      List<RunMetadata.FunctionGraphs> getFunctionGraphsList()
       This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
      repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

    • getFunctionGraphs

      RunMetadata.FunctionGraphs getFunctionGraphs(int index)
       This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
      repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

    • getFunctionGraphsCount

      int getFunctionGraphsCount()
       This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
      repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

    • getFunctionGraphsOrBuilderList

      List<? extends RunMetadata.FunctionGraphsOrBuilder> getFunctionGraphsOrBuilderList()
       This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
      repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

    • getFunctionGraphsOrBuilder

      RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder(int index)
       This is only populated for graphs that are run as functions in TensorFlow
 V2. There will be an entry below for each function that is traced.
 The main use cases of the post_optimization_graph and the partition_graphs
 is to give the caller insight into the graphs that were actually run by the
 runtime. Additional information (such as those in step_stats) will match
 these graphs.
 We also include the pre_optimization_graph since it is usually easier to
 read, and is helpful in situations where the caller wants to get a high
 level idea of what the built graph looks like (since the various graph
 optimization passes might change the structure of the graph significantly).
      repeated .tensorflow.RunMetadata.FunctionGraphs function_graphs = 4;

    • hasSessionMetadata

      boolean hasSessionMetadata()
       Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;
      Returns:
      Whether the sessionMetadata field is set.

    • getSessionMetadata

      SessionMetadata getSessionMetadata()
       Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;
      Returns:
      The sessionMetadata.

    • getSessionMetadataOrBuilder

      SessionMetadataOrBuilder getSessionMetadataOrBuilder()
       Metadata about the session.
      .tensorflow.SessionMetadata session_metadata = 5;