org.tensorflow.framework.RunMetadataOrBuilder Maven / Gradle / Ivy
// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
public interface RunMetadataOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.RunMetadata)
com.google.protobuf.MessageOrBuilder {
/**
*
* 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;
*/
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;
*/
org.tensorflow.framework.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;
*/
org.tensorflow.framework.StepStatsOrBuilder getStepStatsOrBuilder();
/**
*
* The cost graph for the computation defined by the run call.
*
*
* .tensorflow.CostGraphDef cost_graph = 2;
*/
boolean hasCostGraph();
/**
*
* The cost graph for the computation defined by the run call.
*
*
* .tensorflow.CostGraphDef cost_graph = 2;
*/
org.tensorflow.framework.CostGraphDef getCostGraph();
/**
*
* The cost graph for the computation defined by the run call.
*
*
* .tensorflow.CostGraphDef cost_graph = 2;
*/
org.tensorflow.framework.CostGraphDefOrBuilder getCostGraphOrBuilder();
/**
*
* Graphs of the partitions executed by executors.
*
*
* repeated .tensorflow.GraphDef partition_graphs = 3;
*/
java.util.List
getPartitionGraphsList();
/**
*
* Graphs of the partitions executed by executors.
*
*
* repeated .tensorflow.GraphDef partition_graphs = 3;
*/
org.tensorflow.framework.GraphDef getPartitionGraphs(int index);
/**
*
* Graphs of the partitions executed by executors.
*
*
* repeated .tensorflow.GraphDef partition_graphs = 3;
*/
int getPartitionGraphsCount();
/**
*
* Graphs of the partitions executed by executors.
*
*
* repeated .tensorflow.GraphDef partition_graphs = 3;
*/
java.util.List
getPartitionGraphsOrBuilderList();
/**
*
* Graphs of the partitions executed by executors.
*
*
* repeated .tensorflow.GraphDef partition_graphs = 3;
*/
org.tensorflow.framework.GraphDefOrBuilder getPartitionGraphsOrBuilder(
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;
*/
java.util.List
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;
*/
org.tensorflow.framework.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;
*/
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;
*/
java.util.List
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;
*/
org.tensorflow.framework.RunMetadata.FunctionGraphsOrBuilder getFunctionGraphsOrBuilder(
int index);
}
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