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;