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RunMetadataOrBuilder

public interface RunMetadataOrBuilder
Known Indirect Subclasses

Public Methods

abstract CostGraphDef
getCostGraph ()
 The cost graph for the computation defined by the run call.
abstract CostGraphDefOrBuilder
getCostGraphOrBuilder ()
 The cost graph for the computation defined by the run call.
abstract RunMetadata.FunctionGraphs
getFunctionGraphs (int index)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
abstract int
getFunctionGraphsCount ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
abstract List< RunMetadata.FunctionGraphs >
getFunctionGraphsList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
abstract RunMetadata.FunctionGraphsOrBuilder
getFunctionGraphsOrBuilder (int index)
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
abstract List<? extends RunMetadata.FunctionGraphsOrBuilder >
getFunctionGraphsOrBuilderList ()
 This is only populated for graphs that are run as functions in TensorFlow
 V2.
abstract GraphDef
getPartitionGraphs (int index)
 Graphs of the partitions executed by executors.
abstract int
getPartitionGraphsCount ()
 Graphs of the partitions executed by executors.
abstract List< GraphDef >
getPartitionGraphsList ()
 Graphs of the partitions executed by executors.
abstract GraphDefOrBuilder
getPartitionGraphsOrBuilder (int index)
 Graphs of the partitions executed by executors.
abstract List<? extends GraphDefOrBuilder >
getPartitionGraphsOrBuilderList ()
 Graphs of the partitions executed by executors.
abstract StepStats
getStepStats ()
 Statistics traced for this step.
abstract StepStatsOrBuilder
getStepStatsOrBuilder ()
 Statistics traced for this step.
abstract boolean
hasCostGraph ()
 The cost graph for the computation defined by the run call.
abstract boolean
hasStepStats ()
 Statistics traced for this step.

Public Methods

public abstract CostGraphDef getCostGraph ()

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

public abstract CostGraphDefOrBuilder getCostGraphOrBuilder ()

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

public abstract 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;

public abstract 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;

public abstract 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;

public abstract 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;

public abstract 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;

public abstract GraphDef getPartitionGraphs (int index)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public abstract int getPartitionGraphsCount ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public abstract List< GraphDef > getPartitionGraphsList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public abstract GraphDefOrBuilder getPartitionGraphsOrBuilder (int index)

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public abstract List<? extends GraphDefOrBuilder > getPartitionGraphsOrBuilderList ()

 Graphs of the partitions executed by executors.
 
repeated .tensorflow.GraphDef partition_graphs = 3;

public abstract 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;

public abstract 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;

public abstract boolean hasCostGraph ()

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

public abstract 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;