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// Generated by the protocol buffer compiler. DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto
package org.tensorflow.framework;
/**
*
* Options for a single Run() call.
*
*
* Protobuf type {@code tensorflow.RunOptions}
*/
public final class RunOptions extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RunOptions)
RunOptionsOrBuilder {
private static final long serialVersionUID = 0L;
// Use RunOptions.newBuilder() to construct.
private RunOptions(com.google.protobuf.GeneratedMessageV3.Builder builder) {
super(builder);
}
private RunOptions() {
traceLevel_ = 0;
timeoutInMs_ = 0L;
interOpThreadPool_ = 0;
outputPartitionGraphs_ = false;
reportTensorAllocationsUponOom_ = false;
}
@java.lang.Override
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getUnknownFields() {
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}
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
this();
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traceLevel_ = rawValue;
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outputPartitionGraphs_ = input.readBool();
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public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable
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/**
*
* TODO(pbar) Turn this into a TraceOptions proto which allows
* tracing to be controlled in a more orthogonal manner?
*
*
* Protobuf enum {@code tensorflow.RunOptions.TraceLevel}
*/
public enum TraceLevel
implements com.google.protobuf.ProtocolMessageEnum {
/**
* NO_TRACE = 0;
*/
NO_TRACE(0),
/**
* SOFTWARE_TRACE = 1;
*/
SOFTWARE_TRACE(1),
/**
* HARDWARE_TRACE = 2;
*/
HARDWARE_TRACE(2),
/**
* FULL_TRACE = 3;
*/
FULL_TRACE(3),
UNRECOGNIZED(-1),
;
/**
* NO_TRACE = 0;
*/
public static final int NO_TRACE_VALUE = 0;
/**
* SOFTWARE_TRACE = 1;
*/
public static final int SOFTWARE_TRACE_VALUE = 1;
/**
* HARDWARE_TRACE = 2;
*/
public static final int HARDWARE_TRACE_VALUE = 2;
/**
* FULL_TRACE = 3;
*/
public static final int FULL_TRACE_VALUE = 3;
public final int getNumber() {
if (this == UNRECOGNIZED) {
throw new java.lang.IllegalArgumentException(
"Can't get the number of an unknown enum value.");
}
return value;
}
/**
* @deprecated Use {@link #forNumber(int)} instead.
*/
@java.lang.Deprecated
public static TraceLevel valueOf(int value) {
return forNumber(value);
}
public static TraceLevel forNumber(int value) {
switch (value) {
case 0: return NO_TRACE;
case 1: return SOFTWARE_TRACE;
case 2: return HARDWARE_TRACE;
case 3: return FULL_TRACE;
default: return null;
}
}
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public TraceLevel findValueByNumber(int number) {
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return getDescriptor().getValues().get(ordinal());
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getDescriptorForType() {
return getDescriptor();
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public static final com.google.protobuf.Descriptors.EnumDescriptor
getDescriptor() {
return org.tensorflow.framework.RunOptions.getDescriptor().getEnumTypes().get(0);
}
private static final TraceLevel[] VALUES = values();
public static TraceLevel valueOf(
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if (desc.getType() != getDescriptor()) {
throw new java.lang.IllegalArgumentException(
"EnumValueDescriptor is not for this type.");
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return UNRECOGNIZED;
}
return VALUES[desc.getIndex()];
}
private final int value;
private TraceLevel(int value) {
this.value = value;
}
// @@protoc_insertion_point(enum_scope:tensorflow.RunOptions.TraceLevel)
}
public interface ExperimentalOrBuilder extends
// @@protoc_insertion_point(interface_extends:tensorflow.RunOptions.Experimental)
com.google.protobuf.MessageOrBuilder {
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
long getCollectiveGraphKey();
/**
*
* If true, then operations (using the inter-op pool) across all
* session::run() calls will be centrally scheduled, optimizing for (median
* and tail) latency.
* Consider using this option for CPU-bound workloads like inference.
*
*
* bool use_run_handler_pool = 2;
*/
boolean getUseRunHandlerPool();
}
/**
*
* Everything inside Experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* Protobuf type {@code tensorflow.RunOptions.Experimental}
*/
public static final class Experimental extends
com.google.protobuf.GeneratedMessageV3 implements
// @@protoc_insertion_point(message_implements:tensorflow.RunOptions.Experimental)
ExperimentalOrBuilder {
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// Use Experimental.newBuilder() to construct.
private Experimental(com.google.protobuf.GeneratedMessageV3.Builder builder) {
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private Experimental() {
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useRunHandlerPool_ = false;
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getDescriptor() {
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public static final int COLLECTIVE_GRAPH_KEY_FIELD_NUMBER = 1;
private long collectiveGraphKey_;
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public long getCollectiveGraphKey() {
return collectiveGraphKey_;
}
public static final int USE_RUN_HANDLER_POOL_FIELD_NUMBER = 2;
private boolean useRunHandlerPool_;
/**
*
* If true, then operations (using the inter-op pool) across all
* session::run() calls will be centrally scheduled, optimizing for (median
* and tail) latency.
* Consider using this option for CPU-bound workloads like inference.
*
*
* bool use_run_handler_pool = 2;
*/
public boolean getUseRunHandlerPool() {
return useRunHandlerPool_;
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result = result && (getCollectiveGraphKey()
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result = result && (getUseRunHandlerPool()
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result = result && unknownFields.equals(other.unknownFields);
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@java.lang.Override
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if (memoizedHashCode != 0) {
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hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + COLLECTIVE_GRAPH_KEY_FIELD_NUMBER;
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public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
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public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
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public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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public static org.tensorflow.framework.RunOptions.Experimental parseFrom(
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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public Builder newBuilderForType() { return newBuilder(); }
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@java.lang.Override
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/**
*
* Everything inside Experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* Protobuf type {@code tensorflow.RunOptions.Experimental}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RunOptions.Experimental)
org.tensorflow.framework.RunOptions.ExperimentalOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_descriptor;
}
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public Builder clear() {
super.clear();
collectiveGraphKey_ = 0L;
useRunHandlerPool_ = false;
return this;
}
public com.google.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_Experimental_descriptor;
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public org.tensorflow.framework.RunOptions.Experimental buildPartial() {
org.tensorflow.framework.RunOptions.Experimental result = new org.tensorflow.framework.RunOptions.Experimental(this);
result.collectiveGraphKey_ = collectiveGraphKey_;
result.useRunHandlerPool_ = useRunHandlerPool_;
onBuilt();
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public Builder mergeFrom(org.tensorflow.framework.RunOptions.Experimental other) {
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if (other.getCollectiveGraphKey() != 0L) {
setCollectiveGraphKey(other.getCollectiveGraphKey());
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private long collectiveGraphKey_ ;
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public long getCollectiveGraphKey() {
return collectiveGraphKey_;
}
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public Builder setCollectiveGraphKey(long value) {
collectiveGraphKey_ = value;
onChanged();
return this;
}
/**
*
* If non-zero, declares that this graph is going to use collective
* ops and must synchronize step_ids with any other graph with this
* same group_key value (in a distributed computation where tasks
* run disjoint graphs).
*
*
* int64 collective_graph_key = 1;
*/
public Builder clearCollectiveGraphKey() {
collectiveGraphKey_ = 0L;
onChanged();
return this;
}
private boolean useRunHandlerPool_ ;
/**
*
* If true, then operations (using the inter-op pool) across all
* session::run() calls will be centrally scheduled, optimizing for (median
* and tail) latency.
* Consider using this option for CPU-bound workloads like inference.
*
*
* bool use_run_handler_pool = 2;
*/
public boolean getUseRunHandlerPool() {
return useRunHandlerPool_;
}
/**
*
* If true, then operations (using the inter-op pool) across all
* session::run() calls will be centrally scheduled, optimizing for (median
* and tail) latency.
* Consider using this option for CPU-bound workloads like inference.
*
*
* bool use_run_handler_pool = 2;
*/
public Builder setUseRunHandlerPool(boolean value) {
useRunHandlerPool_ = value;
onChanged();
return this;
}
/**
*
* If true, then operations (using the inter-op pool) across all
* session::run() calls will be centrally scheduled, optimizing for (median
* and tail) latency.
* Consider using this option for CPU-bound workloads like inference.
*
*
* bool use_run_handler_pool = 2;
*/
public Builder clearUseRunHandlerPool() {
useRunHandlerPool_ = false;
onChanged();
return this;
}
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
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public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
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}
// @@protoc_insertion_point(builder_scope:tensorflow.RunOptions.Experimental)
}
// @@protoc_insertion_point(class_scope:tensorflow.RunOptions.Experimental)
private static final org.tensorflow.framework.RunOptions.Experimental DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.RunOptions.Experimental();
}
public static org.tensorflow.framework.RunOptions.Experimental getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
public Experimental parsePartialFrom(
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com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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return new Experimental(input, extensionRegistry);
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};
public static com.google.protobuf.Parser parser() {
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public com.google.protobuf.Parser getParserForType() {
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public org.tensorflow.framework.RunOptions.Experimental getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
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}
public static final int TRACE_LEVEL_FIELD_NUMBER = 1;
private int traceLevel_;
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public int getTraceLevelValue() {
return traceLevel_;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public org.tensorflow.framework.RunOptions.TraceLevel getTraceLevel() {
org.tensorflow.framework.RunOptions.TraceLevel result = org.tensorflow.framework.RunOptions.TraceLevel.valueOf(traceLevel_);
return result == null ? org.tensorflow.framework.RunOptions.TraceLevel.UNRECOGNIZED : result;
}
public static final int TIMEOUT_IN_MS_FIELD_NUMBER = 2;
private long timeoutInMs_;
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public long getTimeoutInMs() {
return timeoutInMs_;
}
public static final int INTER_OP_THREAD_POOL_FIELD_NUMBER = 3;
private int interOpThreadPool_;
/**
*
* The thread pool to use, if session_inter_op_thread_pool is configured.
* To use the caller thread set this to -1 - this uses the caller thread
* to execute Session::Run() and thus avoids a context switch. Using the
* caller thread to execute Session::Run() should be done ONLY for simple
* graphs, where the overhead of an additional context switch is
* comparable with the overhead of Session::Run().
*
*
* int32 inter_op_thread_pool = 3;
*/
public int getInterOpThreadPool() {
return interOpThreadPool_;
}
public static final int OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER = 5;
private boolean outputPartitionGraphs_;
/**
*
* Whether the partition graph(s) executed by the executor(s) should be
* outputted via RunMetadata.
*
*
* bool output_partition_graphs = 5;
*/
public boolean getOutputPartitionGraphs() {
return outputPartitionGraphs_;
}
public static final int DEBUG_OPTIONS_FIELD_NUMBER = 6;
private org.tensorflow.framework.DebugOptions debugOptions_;
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public boolean hasDebugOptions() {
return debugOptions_ != null;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptions getDebugOptions() {
return debugOptions_ == null ? org.tensorflow.framework.DebugOptions.getDefaultInstance() : debugOptions_;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptionsOrBuilder getDebugOptionsOrBuilder() {
return getDebugOptions();
}
public static final int REPORT_TENSOR_ALLOCATIONS_UPON_OOM_FIELD_NUMBER = 7;
private boolean reportTensorAllocationsUponOom_;
/**
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
*
* bool report_tensor_allocations_upon_oom = 7;
*/
public boolean getReportTensorAllocationsUponOom() {
return reportTensorAllocationsUponOom_;
}
public static final int EXPERIMENTAL_FIELD_NUMBER = 8;
private org.tensorflow.framework.RunOptions.Experimental experimental_;
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public boolean hasExperimental() {
return experimental_ != null;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.Experimental getExperimental() {
return experimental_ == null ? org.tensorflow.framework.RunOptions.Experimental.getDefaultInstance() : experimental_;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
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output.writeMessage(8, getExperimental());
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@java.lang.Override
public boolean equals(final java.lang.Object obj) {
if (obj == this) {
return true;
}
if (!(obj instanceof org.tensorflow.framework.RunOptions)) {
return super.equals(obj);
}
org.tensorflow.framework.RunOptions other = (org.tensorflow.framework.RunOptions) obj;
boolean result = true;
result = result && traceLevel_ == other.traceLevel_;
result = result && (getTimeoutInMs()
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result = result && (getInterOpThreadPool()
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result = result && (getOutputPartitionGraphs()
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result = result && (hasDebugOptions() == other.hasDebugOptions());
if (hasDebugOptions()) {
result = result && getDebugOptions()
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result = result && (getReportTensorAllocationsUponOom()
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result = result && (hasExperimental() == other.hasExperimental());
if (hasExperimental()) {
result = result && getExperimental()
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result = result && unknownFields.equals(other.unknownFields);
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}
@java.lang.Override
public int hashCode() {
if (memoizedHashCode != 0) {
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int hash = 41;
hash = (19 * hash) + getDescriptor().hashCode();
hash = (37 * hash) + TRACE_LEVEL_FIELD_NUMBER;
hash = (53 * hash) + traceLevel_;
hash = (37 * hash) + TIMEOUT_IN_MS_FIELD_NUMBER;
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hash = (37 * hash) + OUTPUT_PARTITION_GRAPHS_FIELD_NUMBER;
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if (hasDebugOptions()) {
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hash = (53 * hash) + getExperimental().hashCode();
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public static org.tensorflow.framework.RunOptions parseFrom(
java.nio.ByteBuffer data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static org.tensorflow.framework.RunOptions parseFrom(
java.nio.ByteBuffer data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.RunOptions parseFrom(
com.google.protobuf.ByteString data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
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public static org.tensorflow.framework.RunOptions parseFrom(
com.google.protobuf.ByteString data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
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public static org.tensorflow.framework.RunOptions parseFrom(byte[] data)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data);
}
public static org.tensorflow.framework.RunOptions parseFrom(
byte[] data,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return PARSER.parseFrom(data, extensionRegistry);
}
public static org.tensorflow.framework.RunOptions parseFrom(java.io.InputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
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}
public static org.tensorflow.framework.RunOptions parseFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
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return com.google.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RunOptions parseDelimitedFrom(java.io.InputStream input)
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return com.google.protobuf.GeneratedMessageV3
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public static org.tensorflow.framework.RunOptions parseDelimitedFrom(
java.io.InputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseDelimitedWithIOException(PARSER, input, extensionRegistry);
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public static org.tensorflow.framework.RunOptions parseFrom(
com.google.protobuf.CodedInputStream input)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
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}
public static org.tensorflow.framework.RunOptions parseFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
return com.google.protobuf.GeneratedMessageV3
.parseWithIOException(PARSER, input, extensionRegistry);
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public Builder newBuilderForType() { return newBuilder(); }
public static Builder newBuilder() {
return DEFAULT_INSTANCE.toBuilder();
}
public static Builder newBuilder(org.tensorflow.framework.RunOptions prototype) {
return DEFAULT_INSTANCE.toBuilder().mergeFrom(prototype);
}
public Builder toBuilder() {
return this == DEFAULT_INSTANCE
? new Builder() : new Builder().mergeFrom(this);
}
@java.lang.Override
protected Builder newBuilderForType(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
Builder builder = new Builder(parent);
return builder;
}
/**
*
* Options for a single Run() call.
*
*
* Protobuf type {@code tensorflow.RunOptions}
*/
public static final class Builder extends
com.google.protobuf.GeneratedMessageV3.Builder implements
// @@protoc_insertion_point(builder_implements:tensorflow.RunOptions)
org.tensorflow.framework.RunOptionsOrBuilder {
public static final com.google.protobuf.Descriptors.Descriptor
getDescriptor() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
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internalGetFieldAccessorTable() {
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private Builder(
com.google.protobuf.GeneratedMessageV3.BuilderParent parent) {
super(parent);
maybeForceBuilderInitialization();
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public Builder clear() {
super.clear();
traceLevel_ = 0;
timeoutInMs_ = 0L;
interOpThreadPool_ = 0;
outputPartitionGraphs_ = false;
if (debugOptionsBuilder_ == null) {
debugOptions_ = null;
} else {
debugOptions_ = null;
debugOptionsBuilder_ = null;
}
reportTensorAllocationsUponOom_ = false;
if (experimentalBuilder_ == null) {
experimental_ = null;
} else {
experimental_ = null;
experimentalBuilder_ = null;
}
return this;
}
public com.google.protobuf.Descriptors.Descriptor
getDescriptorForType() {
return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_RunOptions_descriptor;
}
public org.tensorflow.framework.RunOptions getDefaultInstanceForType() {
return org.tensorflow.framework.RunOptions.getDefaultInstance();
}
public org.tensorflow.framework.RunOptions build() {
org.tensorflow.framework.RunOptions result = buildPartial();
if (!result.isInitialized()) {
throw newUninitializedMessageException(result);
}
return result;
}
public org.tensorflow.framework.RunOptions buildPartial() {
org.tensorflow.framework.RunOptions result = new org.tensorflow.framework.RunOptions(this);
result.traceLevel_ = traceLevel_;
result.timeoutInMs_ = timeoutInMs_;
result.interOpThreadPool_ = interOpThreadPool_;
result.outputPartitionGraphs_ = outputPartitionGraphs_;
if (debugOptionsBuilder_ == null) {
result.debugOptions_ = debugOptions_;
} else {
result.debugOptions_ = debugOptionsBuilder_.build();
}
result.reportTensorAllocationsUponOom_ = reportTensorAllocationsUponOom_;
if (experimentalBuilder_ == null) {
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} else {
result.experimental_ = experimentalBuilder_.build();
}
onBuilt();
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}
public Builder clone() {
return (Builder) super.clone();
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public Builder setField(
com.google.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
return (Builder) super.setField(field, value);
}
public Builder clearField(
com.google.protobuf.Descriptors.FieldDescriptor field) {
return (Builder) super.clearField(field);
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public Builder clearOneof(
com.google.protobuf.Descriptors.OneofDescriptor oneof) {
return (Builder) super.clearOneof(oneof);
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public Builder setRepeatedField(
com.google.protobuf.Descriptors.FieldDescriptor field,
int index, java.lang.Object value) {
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com.google.protobuf.Descriptors.FieldDescriptor field,
java.lang.Object value) {
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public Builder mergeFrom(com.google.protobuf.Message other) {
if (other instanceof org.tensorflow.framework.RunOptions) {
return mergeFrom((org.tensorflow.framework.RunOptions)other);
} else {
super.mergeFrom(other);
return this;
}
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public Builder mergeFrom(org.tensorflow.framework.RunOptions other) {
if (other == org.tensorflow.framework.RunOptions.getDefaultInstance()) return this;
if (other.traceLevel_ != 0) {
setTraceLevelValue(other.getTraceLevelValue());
}
if (other.getTimeoutInMs() != 0L) {
setTimeoutInMs(other.getTimeoutInMs());
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if (other.getInterOpThreadPool() != 0) {
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mergeDebugOptions(other.getDebugOptions());
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setReportTensorAllocationsUponOom(other.getReportTensorAllocationsUponOom());
}
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mergeExperimental(other.getExperimental());
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this.mergeUnknownFields(other.unknownFields);
onChanged();
return this;
}
public final boolean isInitialized() {
return true;
}
public Builder mergeFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws java.io.IOException {
org.tensorflow.framework.RunOptions parsedMessage = null;
try {
parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry);
} catch (com.google.protobuf.InvalidProtocolBufferException e) {
parsedMessage = (org.tensorflow.framework.RunOptions) e.getUnfinishedMessage();
throw e.unwrapIOException();
} finally {
if (parsedMessage != null) {
mergeFrom(parsedMessage);
}
}
return this;
}
private int traceLevel_ = 0;
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public int getTraceLevelValue() {
return traceLevel_;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder setTraceLevelValue(int value) {
traceLevel_ = value;
onChanged();
return this;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public org.tensorflow.framework.RunOptions.TraceLevel getTraceLevel() {
org.tensorflow.framework.RunOptions.TraceLevel result = org.tensorflow.framework.RunOptions.TraceLevel.valueOf(traceLevel_);
return result == null ? org.tensorflow.framework.RunOptions.TraceLevel.UNRECOGNIZED : result;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder setTraceLevel(org.tensorflow.framework.RunOptions.TraceLevel value) {
if (value == null) {
throw new NullPointerException();
}
traceLevel_ = value.getNumber();
onChanged();
return this;
}
/**
* .tensorflow.RunOptions.TraceLevel trace_level = 1;
*/
public Builder clearTraceLevel() {
traceLevel_ = 0;
onChanged();
return this;
}
private long timeoutInMs_ ;
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public long getTimeoutInMs() {
return timeoutInMs_;
}
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public Builder setTimeoutInMs(long value) {
timeoutInMs_ = value;
onChanged();
return this;
}
/**
*
* Time to wait for operation to complete in milliseconds.
*
*
* int64 timeout_in_ms = 2;
*/
public Builder clearTimeoutInMs() {
timeoutInMs_ = 0L;
onChanged();
return this;
}
private int interOpThreadPool_ ;
/**
*
* The thread pool to use, if session_inter_op_thread_pool is configured.
* To use the caller thread set this to -1 - this uses the caller thread
* to execute Session::Run() and thus avoids a context switch. Using the
* caller thread to execute Session::Run() should be done ONLY for simple
* graphs, where the overhead of an additional context switch is
* comparable with the overhead of Session::Run().
*
*
* int32 inter_op_thread_pool = 3;
*/
public int getInterOpThreadPool() {
return interOpThreadPool_;
}
/**
*
* The thread pool to use, if session_inter_op_thread_pool is configured.
* To use the caller thread set this to -1 - this uses the caller thread
* to execute Session::Run() and thus avoids a context switch. Using the
* caller thread to execute Session::Run() should be done ONLY for simple
* graphs, where the overhead of an additional context switch is
* comparable with the overhead of Session::Run().
*
*
* int32 inter_op_thread_pool = 3;
*/
public Builder setInterOpThreadPool(int value) {
interOpThreadPool_ = value;
onChanged();
return this;
}
/**
*
* The thread pool to use, if session_inter_op_thread_pool is configured.
* To use the caller thread set this to -1 - this uses the caller thread
* to execute Session::Run() and thus avoids a context switch. Using the
* caller thread to execute Session::Run() should be done ONLY for simple
* graphs, where the overhead of an additional context switch is
* comparable with the overhead of Session::Run().
*
*
* int32 inter_op_thread_pool = 3;
*/
public Builder clearInterOpThreadPool() {
interOpThreadPool_ = 0;
onChanged();
return this;
}
private boolean outputPartitionGraphs_ ;
/**
*
* Whether the partition graph(s) executed by the executor(s) should be
* outputted via RunMetadata.
*
*
* bool output_partition_graphs = 5;
*/
public boolean getOutputPartitionGraphs() {
return outputPartitionGraphs_;
}
/**
*
* Whether the partition graph(s) executed by the executor(s) should be
* outputted via RunMetadata.
*
*
* bool output_partition_graphs = 5;
*/
public Builder setOutputPartitionGraphs(boolean value) {
outputPartitionGraphs_ = value;
onChanged();
return this;
}
/**
*
* Whether the partition graph(s) executed by the executor(s) should be
* outputted via RunMetadata.
*
*
* bool output_partition_graphs = 5;
*/
public Builder clearOutputPartitionGraphs() {
outputPartitionGraphs_ = false;
onChanged();
return this;
}
private org.tensorflow.framework.DebugOptions debugOptions_ = null;
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.DebugOptions, org.tensorflow.framework.DebugOptions.Builder, org.tensorflow.framework.DebugOptionsOrBuilder> debugOptionsBuilder_;
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public boolean hasDebugOptions() {
return debugOptionsBuilder_ != null || debugOptions_ != null;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptions getDebugOptions() {
if (debugOptionsBuilder_ == null) {
return debugOptions_ == null ? org.tensorflow.framework.DebugOptions.getDefaultInstance() : debugOptions_;
} else {
return debugOptionsBuilder_.getMessage();
}
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public Builder setDebugOptions(org.tensorflow.framework.DebugOptions value) {
if (debugOptionsBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
debugOptions_ = value;
onChanged();
} else {
debugOptionsBuilder_.setMessage(value);
}
return this;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public Builder setDebugOptions(
org.tensorflow.framework.DebugOptions.Builder builderForValue) {
if (debugOptionsBuilder_ == null) {
debugOptions_ = builderForValue.build();
onChanged();
} else {
debugOptionsBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public Builder mergeDebugOptions(org.tensorflow.framework.DebugOptions value) {
if (debugOptionsBuilder_ == null) {
if (debugOptions_ != null) {
debugOptions_ =
org.tensorflow.framework.DebugOptions.newBuilder(debugOptions_).mergeFrom(value).buildPartial();
} else {
debugOptions_ = value;
}
onChanged();
} else {
debugOptionsBuilder_.mergeFrom(value);
}
return this;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public Builder clearDebugOptions() {
if (debugOptionsBuilder_ == null) {
debugOptions_ = null;
onChanged();
} else {
debugOptions_ = null;
debugOptionsBuilder_ = null;
}
return this;
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptions.Builder getDebugOptionsBuilder() {
onChanged();
return getDebugOptionsFieldBuilder().getBuilder();
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
public org.tensorflow.framework.DebugOptionsOrBuilder getDebugOptionsOrBuilder() {
if (debugOptionsBuilder_ != null) {
return debugOptionsBuilder_.getMessageOrBuilder();
} else {
return debugOptions_ == null ?
org.tensorflow.framework.DebugOptions.getDefaultInstance() : debugOptions_;
}
}
/**
*
* EXPERIMENTAL. Options used to initialize DebuggerState, if enabled.
*
*
* .tensorflow.DebugOptions debug_options = 6;
*/
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.DebugOptions, org.tensorflow.framework.DebugOptions.Builder, org.tensorflow.framework.DebugOptionsOrBuilder>
getDebugOptionsFieldBuilder() {
if (debugOptionsBuilder_ == null) {
debugOptionsBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.DebugOptions, org.tensorflow.framework.DebugOptions.Builder, org.tensorflow.framework.DebugOptionsOrBuilder>(
getDebugOptions(),
getParentForChildren(),
isClean());
debugOptions_ = null;
}
return debugOptionsBuilder_;
}
private boolean reportTensorAllocationsUponOom_ ;
/**
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
*
* bool report_tensor_allocations_upon_oom = 7;
*/
public boolean getReportTensorAllocationsUponOom() {
return reportTensorAllocationsUponOom_;
}
/**
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
*
* bool report_tensor_allocations_upon_oom = 7;
*/
public Builder setReportTensorAllocationsUponOom(boolean value) {
reportTensorAllocationsUponOom_ = value;
onChanged();
return this;
}
/**
*
* When enabled, causes tensor allocation information to be included in
* the error message when the Run() call fails because the allocator ran
* out of memory (OOM).
* Enabling this option can slow down the Run() call.
*
*
* bool report_tensor_allocations_upon_oom = 7;
*/
public Builder clearReportTensorAllocationsUponOom() {
reportTensorAllocationsUponOom_ = false;
onChanged();
return this;
}
private org.tensorflow.framework.RunOptions.Experimental experimental_ = null;
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions.Experimental, org.tensorflow.framework.RunOptions.Experimental.Builder, org.tensorflow.framework.RunOptions.ExperimentalOrBuilder> experimentalBuilder_;
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public boolean hasExperimental() {
return experimentalBuilder_ != null || experimental_ != null;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.Experimental getExperimental() {
if (experimentalBuilder_ == null) {
return experimental_ == null ? org.tensorflow.framework.RunOptions.Experimental.getDefaultInstance() : experimental_;
} else {
return experimentalBuilder_.getMessage();
}
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public Builder setExperimental(org.tensorflow.framework.RunOptions.Experimental value) {
if (experimentalBuilder_ == null) {
if (value == null) {
throw new NullPointerException();
}
experimental_ = value;
onChanged();
} else {
experimentalBuilder_.setMessage(value);
}
return this;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public Builder setExperimental(
org.tensorflow.framework.RunOptions.Experimental.Builder builderForValue) {
if (experimentalBuilder_ == null) {
experimental_ = builderForValue.build();
onChanged();
} else {
experimentalBuilder_.setMessage(builderForValue.build());
}
return this;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public Builder mergeExperimental(org.tensorflow.framework.RunOptions.Experimental value) {
if (experimentalBuilder_ == null) {
if (experimental_ != null) {
experimental_ =
org.tensorflow.framework.RunOptions.Experimental.newBuilder(experimental_).mergeFrom(value).buildPartial();
} else {
experimental_ = value;
}
onChanged();
} else {
experimentalBuilder_.mergeFrom(value);
}
return this;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public Builder clearExperimental() {
if (experimentalBuilder_ == null) {
experimental_ = null;
onChanged();
} else {
experimental_ = null;
experimentalBuilder_ = null;
}
return this;
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.Experimental.Builder getExperimentalBuilder() {
onChanged();
return getExperimentalFieldBuilder().getBuilder();
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
public org.tensorflow.framework.RunOptions.ExperimentalOrBuilder getExperimentalOrBuilder() {
if (experimentalBuilder_ != null) {
return experimentalBuilder_.getMessageOrBuilder();
} else {
return experimental_ == null ?
org.tensorflow.framework.RunOptions.Experimental.getDefaultInstance() : experimental_;
}
}
/**
* .tensorflow.RunOptions.Experimental experimental = 8;
*/
private com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions.Experimental, org.tensorflow.framework.RunOptions.Experimental.Builder, org.tensorflow.framework.RunOptions.ExperimentalOrBuilder>
getExperimentalFieldBuilder() {
if (experimentalBuilder_ == null) {
experimentalBuilder_ = new com.google.protobuf.SingleFieldBuilderV3<
org.tensorflow.framework.RunOptions.Experimental, org.tensorflow.framework.RunOptions.Experimental.Builder, org.tensorflow.framework.RunOptions.ExperimentalOrBuilder>(
getExperimental(),
getParentForChildren(),
isClean());
experimental_ = null;
}
return experimentalBuilder_;
}
public final Builder setUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.setUnknownFieldsProto3(unknownFields);
}
public final Builder mergeUnknownFields(
final com.google.protobuf.UnknownFieldSet unknownFields) {
return super.mergeUnknownFields(unknownFields);
}
// @@protoc_insertion_point(builder_scope:tensorflow.RunOptions)
}
// @@protoc_insertion_point(class_scope:tensorflow.RunOptions)
private static final org.tensorflow.framework.RunOptions DEFAULT_INSTANCE;
static {
DEFAULT_INSTANCE = new org.tensorflow.framework.RunOptions();
}
public static org.tensorflow.framework.RunOptions getDefaultInstance() {
return DEFAULT_INSTANCE;
}
private static final com.google.protobuf.Parser
PARSER = new com.google.protobuf.AbstractParser() {
public RunOptions parsePartialFrom(
com.google.protobuf.CodedInputStream input,
com.google.protobuf.ExtensionRegistryLite extensionRegistry)
throws com.google.protobuf.InvalidProtocolBufferException {
return new RunOptions(input, extensionRegistry);
}
};
public static com.google.protobuf.Parser parser() {
return PARSER;
}
@java.lang.Override
public com.google.protobuf.Parser getParserForType() {
return PARSER;
}
public org.tensorflow.framework.RunOptions getDefaultInstanceForType() {
return DEFAULT_INSTANCE;
}
}
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