Package org.platanios.tensorflow.proto

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 Detail

      • 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.
        .org.platanios.tensorflow.proto.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.
        .org.platanios.tensorflow.proto.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.
        .org.platanios.tensorflow.proto.StepStats step_stats = 1;

      • hasCostGraph

        boolean hasCostGraph()
         The cost graph for the computation defined by the run call.
        .org.platanios.tensorflow.proto.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.
        .org.platanios.tensorflow.proto.CostGraphDef cost_graph = 2;
        Returns:
        The costGraph.

      • getCostGraphOrBuilder

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

      • getPartitionGraphsList

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

      • getPartitionGraphs

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

      • getPartitionGraphsCount

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

      • getPartitionGraphsOrBuilderList

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

      • getPartitionGraphsOrBuilder

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

      • getFunctionGraphsList

        java.util.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 .org.platanios.tensorflow.proto.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 .org.platanios.tensorflow.proto.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 .org.platanios.tensorflow.proto.RunMetadata.FunctionGraphs function_graphs = 4;

      • getFunctionGraphsOrBuilderList

        java.util.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 .org.platanios.tensorflow.proto.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 .org.platanios.tensorflow.proto.RunMetadata.FunctionGraphs function_graphs = 4;