public interface RunMetadataOrBuilder
extends com.google.protobuf.MessageOrBuilder
| Modifier and Type | Method and Description |
|---|---|
CostGraphDef |
getCostGraph()
The cost graph for the computation defined by the run call.
|
CostGraphDefOrBuilder |
getCostGraphOrBuilder()
The cost graph for the computation defined by the run call.
|
RunMetadata.FunctionGraphs |
getFunctionGraphs(int index)
This is only populated for graphs that are run as functions in TensorFlow
V2.
|
int |
getFunctionGraphsCount()
This is only populated for graphs that are run as functions in TensorFlow
V2.
|
List<RunMetadata.FunctionGraphs> |
getFunctionGraphsList()
This is only populated for graphs that are run as functions in TensorFlow
V2.
|
RunMetadata.FunctionGraphsOrBuilder |
getFunctionGraphsOrBuilder(int index)
This is only populated for graphs that are run as functions in TensorFlow
V2.
|
List<? extends RunMetadata.FunctionGraphsOrBuilder> |
getFunctionGraphsOrBuilderList()
This is only populated for graphs that are run as functions in TensorFlow
V2.
|
GraphDef |
getPartitionGraphs(int index)
Graphs of the partitions executed by executors.
|
int |
getPartitionGraphsCount()
Graphs of the partitions executed by executors.
|
List<GraphDef> |
getPartitionGraphsList()
Graphs of the partitions executed by executors.
|
GraphDefOrBuilder |
getPartitionGraphsOrBuilder(int index)
Graphs of the partitions executed by executors.
|
List<? extends GraphDefOrBuilder> |
getPartitionGraphsOrBuilderList()
Graphs of the partitions executed by executors.
|
StepStats |
getStepStats()
Statistics traced for this step.
|
StepStatsOrBuilder |
getStepStatsOrBuilder()
Statistics traced for this step.
|
boolean |
hasCostGraph()
The cost graph for the computation defined by the run call.
|
boolean |
hasStepStats()
Statistics traced for this step.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneofboolean 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;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;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;boolean hasCostGraph()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;CostGraphDef getCostGraph()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;CostGraphDefOrBuilder getCostGraphOrBuilder()
The cost graph for the computation defined by the run call.
.tensorflow.CostGraphDef cost_graph = 2;List<GraphDef> getPartitionGraphsList()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;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;List<? extends GraphDefOrBuilder> getPartitionGraphsOrBuilderList()
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;GraphDefOrBuilder getPartitionGraphsOrBuilder(int index)
Graphs of the partitions executed by executors.
repeated .tensorflow.GraphDef partition_graphs = 3;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;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;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;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;Copyright © 2015–2019. All rights reserved.