All Downloads are FREE. Search and download functionalities are using the official Maven repository.

org.tensorflow.framework.ConfigProto Maven / Gradle / Ivy

There is a newer version: 1.0.0-M2.1
Show newest version
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto

package org.tensorflow.framework;

/**
 * 
 * Session configuration parameters.
 * The system picks appropriate values for fields that are not set.
 * 
* * Protobuf type {@code tensorflow.ConfigProto} */ public final class ConfigProto extends org.nd4j.shade.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:tensorflow.ConfigProto) ConfigProtoOrBuilder { private static final long serialVersionUID = 0L; // Use ConfigProto.newBuilder() to construct. private ConfigProto(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private ConfigProto() { intraOpParallelismThreads_ = 0; interOpParallelismThreads_ = 0; usePerSessionThreads_ = false; sessionInterOpThreadPool_ = java.util.Collections.emptyList(); placementPeriod_ = 0; deviceFilters_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; allowSoftPlacement_ = false; logDevicePlacement_ = false; operationTimeoutInMs_ = 0L; isolateSessionState_ = false; } @java.lang.Override public final org.nd4j.shade.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private ConfigProto( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { this(); if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } int mutable_bitField0_ = 0; org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields = org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownFieldProto3( input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 10: { if (!((mutable_bitField0_ & 0x00000001) == 0x00000001)) { deviceCount_ = org.nd4j.shade.protobuf.MapField.newMapField( DeviceCountDefaultEntryHolder.defaultEntry); mutable_bitField0_ |= 0x00000001; } org.nd4j.shade.protobuf.MapEntry deviceCount__ = input.readMessage( DeviceCountDefaultEntryHolder.defaultEntry.getParserForType(), extensionRegistry); deviceCount_.getMutableMap().put( deviceCount__.getKey(), deviceCount__.getValue()); break; } case 16: { intraOpParallelismThreads_ = input.readInt32(); break; } case 24: { placementPeriod_ = input.readInt32(); break; } case 34: { java.lang.String s = input.readStringRequireUtf8(); if (!((mutable_bitField0_ & 0x00000040) == 0x00000040)) { deviceFilters_ = new org.nd4j.shade.protobuf.LazyStringArrayList(); mutable_bitField0_ |= 0x00000040; } deviceFilters_.add(s); break; } case 40: { interOpParallelismThreads_ = input.readInt32(); break; } case 50: { org.tensorflow.framework.GPUOptions.Builder subBuilder = null; if (gpuOptions_ != null) { subBuilder = gpuOptions_.toBuilder(); } gpuOptions_ = input.readMessage(org.tensorflow.framework.GPUOptions.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(gpuOptions_); gpuOptions_ = subBuilder.buildPartial(); } break; } case 56: { allowSoftPlacement_ = input.readBool(); break; } case 64: { logDevicePlacement_ = input.readBool(); break; } case 72: { usePerSessionThreads_ = input.readBool(); break; } case 82: { org.tensorflow.framework.GraphOptions.Builder subBuilder = null; if (graphOptions_ != null) { subBuilder = graphOptions_.toBuilder(); } graphOptions_ = input.readMessage(org.tensorflow.framework.GraphOptions.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(graphOptions_); graphOptions_ = subBuilder.buildPartial(); } break; } case 88: { operationTimeoutInMs_ = input.readInt64(); break; } case 98: { if (!((mutable_bitField0_ & 0x00000010) == 0x00000010)) { sessionInterOpThreadPool_ = new java.util.ArrayList(); mutable_bitField0_ |= 0x00000010; } sessionInterOpThreadPool_.add( input.readMessage(org.tensorflow.framework.ThreadPoolOptionProto.parser(), extensionRegistry)); break; } case 106: { org.tensorflow.framework.RPCOptions.Builder subBuilder = null; if (rpcOptions_ != null) { subBuilder = rpcOptions_.toBuilder(); } rpcOptions_ = input.readMessage(org.tensorflow.framework.RPCOptions.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(rpcOptions_); rpcOptions_ = subBuilder.buildPartial(); } break; } case 114: { org.tensorflow.distruntime.ClusterDef.Builder subBuilder = null; if (clusterDef_ != null) { subBuilder = clusterDef_.toBuilder(); } clusterDef_ = input.readMessage(org.tensorflow.distruntime.ClusterDef.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(clusterDef_); clusterDef_ = subBuilder.buildPartial(); } break; } case 120: { isolateSessionState_ = input.readBool(); break; } case 130: { org.tensorflow.framework.ConfigProto.Experimental.Builder subBuilder = null; if (experimental_ != null) { subBuilder = experimental_.toBuilder(); } experimental_ = input.readMessage(org.tensorflow.framework.ConfigProto.Experimental.parser(), extensionRegistry); if (subBuilder != null) { subBuilder.mergeFrom(experimental_); experimental_ = subBuilder.buildPartial(); } break; } } } } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { throw e.setUnfinishedMessage(this); } catch (java.io.IOException e) { throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { if (((mutable_bitField0_ & 0x00000040) == 0x00000040)) { deviceFilters_ = deviceFilters_.getUnmodifiableView(); } if (((mutable_bitField0_ & 0x00000010) == 0x00000010)) { sessionInterOpThreadPool_ = java.util.Collections.unmodifiableList(sessionInterOpThreadPool_); } this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_descriptor; } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMapField( int number) { switch (number) { case 1: return internalGetDeviceCount(); default: throw new RuntimeException( "Invalid map field number: " + number); } } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.ConfigProto.class, org.tensorflow.framework.ConfigProto.Builder.class); } public interface ExperimentalOrBuilder extends // @@protoc_insertion_point(interface_extends:tensorflow.ConfigProto.Experimental) org.nd4j.shade.protobuf.MessageOrBuilder { /** *
     * Task name for group resolution.
     * 
* * string collective_group_leader = 1; */ java.lang.String getCollectiveGroupLeader(); /** *
     * Task name for group resolution.
     * 
* * string collective_group_leader = 1; */ org.nd4j.shade.protobuf.ByteString getCollectiveGroupLeaderBytes(); } /** *
   * 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.ConfigProto.Experimental} */ public static final class Experimental extends org.nd4j.shade.protobuf.GeneratedMessageV3 implements // @@protoc_insertion_point(message_implements:tensorflow.ConfigProto.Experimental) ExperimentalOrBuilder { private static final long serialVersionUID = 0L; // Use Experimental.newBuilder() to construct. private Experimental(org.nd4j.shade.protobuf.GeneratedMessageV3.Builder builder) { super(builder); } private Experimental() { collectiveGroupLeader_ = ""; } @java.lang.Override public final org.nd4j.shade.protobuf.UnknownFieldSet getUnknownFields() { return this.unknownFields; } private Experimental( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { this(); if (extensionRegistry == null) { throw new java.lang.NullPointerException(); } int mutable_bitField0_ = 0; org.nd4j.shade.protobuf.UnknownFieldSet.Builder unknownFields = org.nd4j.shade.protobuf.UnknownFieldSet.newBuilder(); try { boolean done = false; while (!done) { int tag = input.readTag(); switch (tag) { case 0: done = true; break; default: { if (!parseUnknownFieldProto3( input, unknownFields, extensionRegistry, tag)) { done = true; } break; } case 10: { java.lang.String s = input.readStringRequireUtf8(); collectiveGroupLeader_ = s; break; } } } } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { throw e.setUnfinishedMessage(this); } catch (java.io.IOException e) { throw new org.nd4j.shade.protobuf.InvalidProtocolBufferException( e).setUnfinishedMessage(this); } finally { this.unknownFields = unknownFields.build(); makeExtensionsImmutable(); } } public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_Experimental_descriptor; } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_Experimental_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.ConfigProto.Experimental.class, org.tensorflow.framework.ConfigProto.Experimental.Builder.class); } public static final int COLLECTIVE_GROUP_LEADER_FIELD_NUMBER = 1; private volatile java.lang.Object collectiveGroupLeader_; /** *
     * Task name for group resolution.
     * 
* * string collective_group_leader = 1; */ public java.lang.String getCollectiveGroupLeader() { java.lang.Object ref = collectiveGroupLeader_; if (ref instanceof java.lang.String) { return (java.lang.String) ref; } else { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); collectiveGroupLeader_ = s; return s; } } /** *
     * Task name for group resolution.
     * 
* * string collective_group_leader = 1; */ public org.nd4j.shade.protobuf.ByteString getCollectiveGroupLeaderBytes() { java.lang.Object ref = collectiveGroupLeader_; if (ref instanceof java.lang.String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); collectiveGroupLeader_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output) throws java.io.IOException { if (!getCollectiveGroupLeaderBytes().isEmpty()) { org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 1, collectiveGroupLeader_); } unknownFields.writeTo(output); } public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; if (!getCollectiveGroupLeaderBytes().isEmpty()) { size += org.nd4j.shade.protobuf.GeneratedMessageV3.computeStringSize(1, collectiveGroupLeader_); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.ConfigProto.Experimental)) { return super.equals(obj); } org.tensorflow.framework.ConfigProto.Experimental other = (org.tensorflow.framework.ConfigProto.Experimental) obj; boolean result = true; result = result && getCollectiveGroupLeader() .equals(other.getCollectiveGroupLeader()); result = result && unknownFields.equals(other.unknownFields); return result; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); hash = (37 * hash) + COLLECTIVE_GROUP_LEADER_FIELD_NUMBER; hash = (53 * hash) + getCollectiveGroupLeader().hashCode(); hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( java.nio.ByteBuffer data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( java.nio.ByteBuffer data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( org.nd4j.shade.protobuf.ByteString data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( org.nd4j.shade.protobuf.ByteString data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom(byte[] data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( byte[] data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.ConfigProto.Experimental parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto.Experimental parseDelimitedFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( org.nd4j.shade.protobuf.CodedInputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto.Experimental parseFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.framework.ConfigProto.Experimental 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( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** *
     * 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.ConfigProto.Experimental} */ public static final class Builder extends org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.ConfigProto.Experimental) org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder { public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_Experimental_descriptor; } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_Experimental_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.ConfigProto.Experimental.class, org.tensorflow.framework.ConfigProto.Experimental.Builder.class); } // Construct using org.tensorflow.framework.ConfigProto.Experimental.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (org.nd4j.shade.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { } } public Builder clear() { super.clear(); collectiveGroupLeader_ = ""; return this; } public org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_Experimental_descriptor; } public org.tensorflow.framework.ConfigProto.Experimental getDefaultInstanceForType() { return org.tensorflow.framework.ConfigProto.Experimental.getDefaultInstance(); } public org.tensorflow.framework.ConfigProto.Experimental build() { org.tensorflow.framework.ConfigProto.Experimental result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } public org.tensorflow.framework.ConfigProto.Experimental buildPartial() { org.tensorflow.framework.ConfigProto.Experimental result = new org.tensorflow.framework.ConfigProto.Experimental(this); result.collectiveGroupLeader_ = collectiveGroupLeader_; onBuilt(); return result; } public Builder clone() { return (Builder) super.clone(); } public Builder setField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) { return (Builder) super.clearField(field); } public Builder clearOneof( org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) { return (Builder) super.clearOneof(oneof); } public Builder setRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) { if (other instanceof org.tensorflow.framework.ConfigProto.Experimental) { return mergeFrom((org.tensorflow.framework.ConfigProto.Experimental)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.framework.ConfigProto.Experimental other) { if (other == org.tensorflow.framework.ConfigProto.Experimental.getDefaultInstance()) return this; if (!other.getCollectiveGroupLeader().isEmpty()) { collectiveGroupLeader_ = other.collectiveGroupLeader_; onChanged(); } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } public final boolean isInitialized() { return true; } public Builder mergeFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { org.tensorflow.framework.ConfigProto.Experimental parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { parsedMessage = (org.tensorflow.framework.ConfigProto.Experimental) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { mergeFrom(parsedMessage); } } return this; } private java.lang.Object collectiveGroupLeader_ = ""; /** *
       * Task name for group resolution.
       * 
* * string collective_group_leader = 1; */ public java.lang.String getCollectiveGroupLeader() { java.lang.Object ref = collectiveGroupLeader_; if (!(ref instanceof java.lang.String)) { org.nd4j.shade.protobuf.ByteString bs = (org.nd4j.shade.protobuf.ByteString) ref; java.lang.String s = bs.toStringUtf8(); collectiveGroupLeader_ = s; return s; } else { return (java.lang.String) ref; } } /** *
       * Task name for group resolution.
       * 
* * string collective_group_leader = 1; */ public org.nd4j.shade.protobuf.ByteString getCollectiveGroupLeaderBytes() { java.lang.Object ref = collectiveGroupLeader_; if (ref instanceof String) { org.nd4j.shade.protobuf.ByteString b = org.nd4j.shade.protobuf.ByteString.copyFromUtf8( (java.lang.String) ref); collectiveGroupLeader_ = b; return b; } else { return (org.nd4j.shade.protobuf.ByteString) ref; } } /** *
       * Task name for group resolution.
       * 
* * string collective_group_leader = 1; */ public Builder setCollectiveGroupLeader( java.lang.String value) { if (value == null) { throw new NullPointerException(); } collectiveGroupLeader_ = value; onChanged(); return this; } /** *
       * Task name for group resolution.
       * 
* * string collective_group_leader = 1; */ public Builder clearCollectiveGroupLeader() { collectiveGroupLeader_ = getDefaultInstance().getCollectiveGroupLeader(); onChanged(); return this; } /** *
       * Task name for group resolution.
       * 
* * string collective_group_leader = 1; */ public Builder setCollectiveGroupLeaderBytes( org.nd4j.shade.protobuf.ByteString value) { if (value == null) { throw new NullPointerException(); } checkByteStringIsUtf8(value); collectiveGroupLeader_ = value; onChanged(); return this; } public final Builder setUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.mergeUnknownFields(unknownFields); } // @@protoc_insertion_point(builder_scope:tensorflow.ConfigProto.Experimental) } // @@protoc_insertion_point(class_scope:tensorflow.ConfigProto.Experimental) private static final org.tensorflow.framework.ConfigProto.Experimental DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.framework.ConfigProto.Experimental(); } public static org.tensorflow.framework.ConfigProto.Experimental getDefaultInstance() { return DEFAULT_INSTANCE; } private static final org.nd4j.shade.protobuf.Parser PARSER = new org.nd4j.shade.protobuf.AbstractParser() { public Experimental parsePartialFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return new Experimental(input, extensionRegistry); } }; public static org.nd4j.shade.protobuf.Parser parser() { return PARSER; } @java.lang.Override public org.nd4j.shade.protobuf.Parser getParserForType() { return PARSER; } public org.tensorflow.framework.ConfigProto.Experimental getDefaultInstanceForType() { return DEFAULT_INSTANCE; } } private int bitField0_; public static final int DEVICE_COUNT_FIELD_NUMBER = 1; private static final class DeviceCountDefaultEntryHolder { static final org.nd4j.shade.protobuf.MapEntry< java.lang.String, java.lang.Integer> defaultEntry = org.nd4j.shade.protobuf.MapEntry .newDefaultInstance( org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_DeviceCountEntry_descriptor, org.nd4j.shade.protobuf.WireFormat.FieldType.STRING, "", org.nd4j.shade.protobuf.WireFormat.FieldType.INT32, 0); } private org.nd4j.shade.protobuf.MapField< java.lang.String, java.lang.Integer> deviceCount_; private org.nd4j.shade.protobuf.MapField internalGetDeviceCount() { if (deviceCount_ == null) { return org.nd4j.shade.protobuf.MapField.emptyMapField( DeviceCountDefaultEntryHolder.defaultEntry); } return deviceCount_; } public int getDeviceCountCount() { return internalGetDeviceCount().getMap().size(); } /** *
   * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
   * number of devices of that type to use.  If a particular device
   * type is not found in the map, the system picks an appropriate
   * number.
   * 
* * map<string, int32> device_count = 1; */ public boolean containsDeviceCount( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } return internalGetDeviceCount().getMap().containsKey(key); } /** * Use {@link #getDeviceCountMap()} instead. */ @java.lang.Deprecated public java.util.Map getDeviceCount() { return getDeviceCountMap(); } /** *
   * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
   * number of devices of that type to use.  If a particular device
   * type is not found in the map, the system picks an appropriate
   * number.
   * 
* * map<string, int32> device_count = 1; */ public java.util.Map getDeviceCountMap() { return internalGetDeviceCount().getMap(); } /** *
   * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
   * number of devices of that type to use.  If a particular device
   * type is not found in the map, the system picks an appropriate
   * number.
   * 
* * map<string, int32> device_count = 1; */ public int getDeviceCountOrDefault( java.lang.String key, int defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetDeviceCount().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** *
   * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
   * number of devices of that type to use.  If a particular device
   * type is not found in the map, the system picks an appropriate
   * number.
   * 
* * map<string, int32> device_count = 1; */ public int getDeviceCountOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetDeviceCount().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); } return map.get(key); } public static final int INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER = 2; private int intraOpParallelismThreads_; /** *
   * The execution of an individual op (for some op types) can be
   * parallelized on a pool of intra_op_parallelism_threads.
   * 0 means the system picks an appropriate number.
   * 
* * int32 intra_op_parallelism_threads = 2; */ public int getIntraOpParallelismThreads() { return intraOpParallelismThreads_; } public static final int INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER = 5; private int interOpParallelismThreads_; /** *
   * Nodes that perform blocking operations are enqueued on a pool of
   * inter_op_parallelism_threads available in each process.
   * 0 means the system picks an appropriate number.
   * Note that the first Session created in the process sets the
   * number of threads for all future sessions unless use_per_session_threads is
   * true or session_inter_op_thread_pool is configured.
   * 
* * int32 inter_op_parallelism_threads = 5; */ public int getInterOpParallelismThreads() { return interOpParallelismThreads_; } public static final int USE_PER_SESSION_THREADS_FIELD_NUMBER = 9; private boolean usePerSessionThreads_; /** *
   * If true, use a new set of threads for this session rather than the global
   * pool of threads. Only supported by direct sessions.
   * If false, use the global threads created by the first session, or the
   * per-session thread pools configured by session_inter_op_thread_pool.
   * This option is deprecated. The same effect can be achieved by setting
   * session_inter_op_thread_pool to have one element, whose num_threads equals
   * inter_op_parallelism_threads.
   * 
* * bool use_per_session_threads = 9; */ public boolean getUsePerSessionThreads() { return usePerSessionThreads_; } public static final int SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER = 12; private java.util.List sessionInterOpThreadPool_; /** *
   * This option is experimental - it may be replaced with a different mechanism
   * in the future.
   * Configures session thread pools. If this is configured, then RunOptions for
   * a Run call can select the thread pool to use.
   * The intended use is for when some session invocations need to run in a
   * background pool limited to a small number of threads:
   * - For example, a session may be configured to have one large pool (for
   * regular compute) and one small pool (for periodic, low priority work);
   * using the small pool is currently the mechanism for limiting the inter-op
   * parallelism of the low priority work.  Note that it does not limit the
   * parallelism of work spawned by a single op kernel implementation.
   * - Using this setting is normally not needed in training, but may help some
   * serving use cases.
   * - It is also generally recommended to set the global_name field of this
   * proto, to avoid creating multiple large pools. It is typically better to
   * run the non-low-priority work, even across sessions, in a single large
   * pool.
   * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public java.util.List getSessionInterOpThreadPoolList() { return sessionInterOpThreadPool_; } /** *
   * This option is experimental - it may be replaced with a different mechanism
   * in the future.
   * Configures session thread pools. If this is configured, then RunOptions for
   * a Run call can select the thread pool to use.
   * The intended use is for when some session invocations need to run in a
   * background pool limited to a small number of threads:
   * - For example, a session may be configured to have one large pool (for
   * regular compute) and one small pool (for periodic, low priority work);
   * using the small pool is currently the mechanism for limiting the inter-op
   * parallelism of the low priority work.  Note that it does not limit the
   * parallelism of work spawned by a single op kernel implementation.
   * - Using this setting is normally not needed in training, but may help some
   * serving use cases.
   * - It is also generally recommended to set the global_name field of this
   * proto, to avoid creating multiple large pools. It is typically better to
   * run the non-low-priority work, even across sessions, in a single large
   * pool.
   * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public java.util.List getSessionInterOpThreadPoolOrBuilderList() { return sessionInterOpThreadPool_; } /** *
   * This option is experimental - it may be replaced with a different mechanism
   * in the future.
   * Configures session thread pools. If this is configured, then RunOptions for
   * a Run call can select the thread pool to use.
   * The intended use is for when some session invocations need to run in a
   * background pool limited to a small number of threads:
   * - For example, a session may be configured to have one large pool (for
   * regular compute) and one small pool (for periodic, low priority work);
   * using the small pool is currently the mechanism for limiting the inter-op
   * parallelism of the low priority work.  Note that it does not limit the
   * parallelism of work spawned by a single op kernel implementation.
   * - Using this setting is normally not needed in training, but may help some
   * serving use cases.
   * - It is also generally recommended to set the global_name field of this
   * proto, to avoid creating multiple large pools. It is typically better to
   * run the non-low-priority work, even across sessions, in a single large
   * pool.
   * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public int getSessionInterOpThreadPoolCount() { return sessionInterOpThreadPool_.size(); } /** *
   * This option is experimental - it may be replaced with a different mechanism
   * in the future.
   * Configures session thread pools. If this is configured, then RunOptions for
   * a Run call can select the thread pool to use.
   * The intended use is for when some session invocations need to run in a
   * background pool limited to a small number of threads:
   * - For example, a session may be configured to have one large pool (for
   * regular compute) and one small pool (for periodic, low priority work);
   * using the small pool is currently the mechanism for limiting the inter-op
   * parallelism of the low priority work.  Note that it does not limit the
   * parallelism of work spawned by a single op kernel implementation.
   * - Using this setting is normally not needed in training, but may help some
   * serving use cases.
   * - It is also generally recommended to set the global_name field of this
   * proto, to avoid creating multiple large pools. It is typically better to
   * run the non-low-priority work, even across sessions, in a single large
   * pool.
   * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProto getSessionInterOpThreadPool(int index) { return sessionInterOpThreadPool_.get(index); } /** *
   * This option is experimental - it may be replaced with a different mechanism
   * in the future.
   * Configures session thread pools. If this is configured, then RunOptions for
   * a Run call can select the thread pool to use.
   * The intended use is for when some session invocations need to run in a
   * background pool limited to a small number of threads:
   * - For example, a session may be configured to have one large pool (for
   * regular compute) and one small pool (for periodic, low priority work);
   * using the small pool is currently the mechanism for limiting the inter-op
   * parallelism of the low priority work.  Note that it does not limit the
   * parallelism of work spawned by a single op kernel implementation.
   * - Using this setting is normally not needed in training, but may help some
   * serving use cases.
   * - It is also generally recommended to set the global_name field of this
   * proto, to avoid creating multiple large pools. It is typically better to
   * run the non-low-priority work, even across sessions, in a single large
   * pool.
   * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder( int index) { return sessionInterOpThreadPool_.get(index); } public static final int PLACEMENT_PERIOD_FIELD_NUMBER = 3; private int placementPeriod_; /** *
   * Assignment of Nodes to Devices is recomputed every placement_period
   * steps until the system warms up (at which point the recomputation
   * typically slows down automatically).
   * 
* * int32 placement_period = 3; */ public int getPlacementPeriod() { return placementPeriod_; } public static final int DEVICE_FILTERS_FIELD_NUMBER = 4; private org.nd4j.shade.protobuf.LazyStringList deviceFilters_; /** *
   * When any filters are present sessions will ignore all devices which do not
   * match the filters. Each filter can be partially specified, e.g. "/job:ps"
   * "/job:worker/replica:3", etc.
   * 
* * repeated string device_filters = 4; */ public org.nd4j.shade.protobuf.ProtocolStringList getDeviceFiltersList() { return deviceFilters_; } /** *
   * When any filters are present sessions will ignore all devices which do not
   * match the filters. Each filter can be partially specified, e.g. "/job:ps"
   * "/job:worker/replica:3", etc.
   * 
* * repeated string device_filters = 4; */ public int getDeviceFiltersCount() { return deviceFilters_.size(); } /** *
   * When any filters are present sessions will ignore all devices which do not
   * match the filters. Each filter can be partially specified, e.g. "/job:ps"
   * "/job:worker/replica:3", etc.
   * 
* * repeated string device_filters = 4; */ public java.lang.String getDeviceFilters(int index) { return deviceFilters_.get(index); } /** *
   * When any filters are present sessions will ignore all devices which do not
   * match the filters. Each filter can be partially specified, e.g. "/job:ps"
   * "/job:worker/replica:3", etc.
   * 
* * repeated string device_filters = 4; */ public org.nd4j.shade.protobuf.ByteString getDeviceFiltersBytes(int index) { return deviceFilters_.getByteString(index); } public static final int GPU_OPTIONS_FIELD_NUMBER = 6; private org.tensorflow.framework.GPUOptions gpuOptions_; /** *
   * Options that apply to all GPUs.
   * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public boolean hasGpuOptions() { return gpuOptions_ != null; } /** *
   * Options that apply to all GPUs.
   * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public org.tensorflow.framework.GPUOptions getGpuOptions() { return gpuOptions_ == null ? org.tensorflow.framework.GPUOptions.getDefaultInstance() : gpuOptions_; } /** *
   * Options that apply to all GPUs.
   * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public org.tensorflow.framework.GPUOptionsOrBuilder getGpuOptionsOrBuilder() { return getGpuOptions(); } public static final int ALLOW_SOFT_PLACEMENT_FIELD_NUMBER = 7; private boolean allowSoftPlacement_; /** *
   * Whether soft placement is allowed. If allow_soft_placement is true,
   * an op will be placed on CPU if
   *   1. there's no GPU implementation for the OP
   * or
   *   2. no GPU devices are known or registered
   * or
   *   3. need to co-locate with reftype input(s) which are from CPU.
   * 
* * bool allow_soft_placement = 7; */ public boolean getAllowSoftPlacement() { return allowSoftPlacement_; } public static final int LOG_DEVICE_PLACEMENT_FIELD_NUMBER = 8; private boolean logDevicePlacement_; /** *
   * Whether device placements should be logged.
   * 
* * bool log_device_placement = 8; */ public boolean getLogDevicePlacement() { return logDevicePlacement_; } public static final int GRAPH_OPTIONS_FIELD_NUMBER = 10; private org.tensorflow.framework.GraphOptions graphOptions_; /** *
   * Options that apply to all graphs.
   * 
* * .tensorflow.GraphOptions graph_options = 10; */ public boolean hasGraphOptions() { return graphOptions_ != null; } /** *
   * Options that apply to all graphs.
   * 
* * .tensorflow.GraphOptions graph_options = 10; */ public org.tensorflow.framework.GraphOptions getGraphOptions() { return graphOptions_ == null ? org.tensorflow.framework.GraphOptions.getDefaultInstance() : graphOptions_; } /** *
   * Options that apply to all graphs.
   * 
* * .tensorflow.GraphOptions graph_options = 10; */ public org.tensorflow.framework.GraphOptionsOrBuilder getGraphOptionsOrBuilder() { return getGraphOptions(); } public static final int OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER = 11; private long operationTimeoutInMs_; /** *
   * Global timeout for all blocking operations in this session.  If non-zero,
   * and not overridden on a per-operation basis, this value will be used as the
   * deadline for all blocking operations.
   * 
* * int64 operation_timeout_in_ms = 11; */ public long getOperationTimeoutInMs() { return operationTimeoutInMs_; } public static final int RPC_OPTIONS_FIELD_NUMBER = 13; private org.tensorflow.framework.RPCOptions rpcOptions_; /** *
   * Options that apply when this session uses the distributed runtime.
   * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public boolean hasRpcOptions() { return rpcOptions_ != null; } /** *
   * Options that apply when this session uses the distributed runtime.
   * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public org.tensorflow.framework.RPCOptions getRpcOptions() { return rpcOptions_ == null ? org.tensorflow.framework.RPCOptions.getDefaultInstance() : rpcOptions_; } /** *
   * Options that apply when this session uses the distributed runtime.
   * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public org.tensorflow.framework.RPCOptionsOrBuilder getRpcOptionsOrBuilder() { return getRpcOptions(); } public static final int CLUSTER_DEF_FIELD_NUMBER = 14; private org.tensorflow.distruntime.ClusterDef clusterDef_; /** *
   * Optional list of all workers to use in this session.
   * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public boolean hasClusterDef() { return clusterDef_ != null; } /** *
   * Optional list of all workers to use in this session.
   * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public org.tensorflow.distruntime.ClusterDef getClusterDef() { return clusterDef_ == null ? org.tensorflow.distruntime.ClusterDef.getDefaultInstance() : clusterDef_; } /** *
   * Optional list of all workers to use in this session.
   * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public org.tensorflow.distruntime.ClusterDefOrBuilder getClusterDefOrBuilder() { return getClusterDef(); } public static final int ISOLATE_SESSION_STATE_FIELD_NUMBER = 15; private boolean isolateSessionState_; /** *
   * If true, any resources such as Variables used in the session will not be
   * shared with other sessions.
   * 
* * bool isolate_session_state = 15; */ public boolean getIsolateSessionState() { return isolateSessionState_; } public static final int EXPERIMENTAL_FIELD_NUMBER = 16; private org.tensorflow.framework.ConfigProto.Experimental experimental_; /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public boolean hasExperimental() { return experimental_ != null; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public org.tensorflow.framework.ConfigProto.Experimental getExperimental() { return experimental_ == null ? org.tensorflow.framework.ConfigProto.Experimental.getDefaultInstance() : experimental_; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder() { return getExperimental(); } private byte memoizedIsInitialized = -1; public final boolean isInitialized() { byte isInitialized = memoizedIsInitialized; if (isInitialized == 1) return true; if (isInitialized == 0) return false; memoizedIsInitialized = 1; return true; } public void writeTo(org.nd4j.shade.protobuf.CodedOutputStream output) throws java.io.IOException { org.nd4j.shade.protobuf.GeneratedMessageV3 .serializeStringMapTo( output, internalGetDeviceCount(), DeviceCountDefaultEntryHolder.defaultEntry, 1); if (intraOpParallelismThreads_ != 0) { output.writeInt32(2, intraOpParallelismThreads_); } if (placementPeriod_ != 0) { output.writeInt32(3, placementPeriod_); } for (int i = 0; i < deviceFilters_.size(); i++) { org.nd4j.shade.protobuf.GeneratedMessageV3.writeString(output, 4, deviceFilters_.getRaw(i)); } if (interOpParallelismThreads_ != 0) { output.writeInt32(5, interOpParallelismThreads_); } if (gpuOptions_ != null) { output.writeMessage(6, getGpuOptions()); } if (allowSoftPlacement_ != false) { output.writeBool(7, allowSoftPlacement_); } if (logDevicePlacement_ != false) { output.writeBool(8, logDevicePlacement_); } if (usePerSessionThreads_ != false) { output.writeBool(9, usePerSessionThreads_); } if (graphOptions_ != null) { output.writeMessage(10, getGraphOptions()); } if (operationTimeoutInMs_ != 0L) { output.writeInt64(11, operationTimeoutInMs_); } for (int i = 0; i < sessionInterOpThreadPool_.size(); i++) { output.writeMessage(12, sessionInterOpThreadPool_.get(i)); } if (rpcOptions_ != null) { output.writeMessage(13, getRpcOptions()); } if (clusterDef_ != null) { output.writeMessage(14, getClusterDef()); } if (isolateSessionState_ != false) { output.writeBool(15, isolateSessionState_); } if (experimental_ != null) { output.writeMessage(16, getExperimental()); } unknownFields.writeTo(output); } public int getSerializedSize() { int size = memoizedSize; if (size != -1) return size; size = 0; for (java.util.Map.Entry entry : internalGetDeviceCount().getMap().entrySet()) { org.nd4j.shade.protobuf.MapEntry deviceCount__ = DeviceCountDefaultEntryHolder.defaultEntry.newBuilderForType() .setKey(entry.getKey()) .setValue(entry.getValue()) .build(); size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(1, deviceCount__); } if (intraOpParallelismThreads_ != 0) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeInt32Size(2, intraOpParallelismThreads_); } if (placementPeriod_ != 0) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeInt32Size(3, placementPeriod_); } { int dataSize = 0; for (int i = 0; i < deviceFilters_.size(); i++) { dataSize += computeStringSizeNoTag(deviceFilters_.getRaw(i)); } size += dataSize; size += 1 * getDeviceFiltersList().size(); } if (interOpParallelismThreads_ != 0) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeInt32Size(5, interOpParallelismThreads_); } if (gpuOptions_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(6, getGpuOptions()); } if (allowSoftPlacement_ != false) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeBoolSize(7, allowSoftPlacement_); } if (logDevicePlacement_ != false) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeBoolSize(8, logDevicePlacement_); } if (usePerSessionThreads_ != false) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeBoolSize(9, usePerSessionThreads_); } if (graphOptions_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(10, getGraphOptions()); } if (operationTimeoutInMs_ != 0L) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeInt64Size(11, operationTimeoutInMs_); } for (int i = 0; i < sessionInterOpThreadPool_.size(); i++) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(12, sessionInterOpThreadPool_.get(i)); } if (rpcOptions_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(13, getRpcOptions()); } if (clusterDef_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(14, getClusterDef()); } if (isolateSessionState_ != false) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeBoolSize(15, isolateSessionState_); } if (experimental_ != null) { size += org.nd4j.shade.protobuf.CodedOutputStream .computeMessageSize(16, getExperimental()); } size += unknownFields.getSerializedSize(); memoizedSize = size; return size; } @java.lang.Override public boolean equals(final java.lang.Object obj) { if (obj == this) { return true; } if (!(obj instanceof org.tensorflow.framework.ConfigProto)) { return super.equals(obj); } org.tensorflow.framework.ConfigProto other = (org.tensorflow.framework.ConfigProto) obj; boolean result = true; result = result && internalGetDeviceCount().equals( other.internalGetDeviceCount()); result = result && (getIntraOpParallelismThreads() == other.getIntraOpParallelismThreads()); result = result && (getInterOpParallelismThreads() == other.getInterOpParallelismThreads()); result = result && (getUsePerSessionThreads() == other.getUsePerSessionThreads()); result = result && getSessionInterOpThreadPoolList() .equals(other.getSessionInterOpThreadPoolList()); result = result && (getPlacementPeriod() == other.getPlacementPeriod()); result = result && getDeviceFiltersList() .equals(other.getDeviceFiltersList()); result = result && (hasGpuOptions() == other.hasGpuOptions()); if (hasGpuOptions()) { result = result && getGpuOptions() .equals(other.getGpuOptions()); } result = result && (getAllowSoftPlacement() == other.getAllowSoftPlacement()); result = result && (getLogDevicePlacement() == other.getLogDevicePlacement()); result = result && (hasGraphOptions() == other.hasGraphOptions()); if (hasGraphOptions()) { result = result && getGraphOptions() .equals(other.getGraphOptions()); } result = result && (getOperationTimeoutInMs() == other.getOperationTimeoutInMs()); result = result && (hasRpcOptions() == other.hasRpcOptions()); if (hasRpcOptions()) { result = result && getRpcOptions() .equals(other.getRpcOptions()); } result = result && (hasClusterDef() == other.hasClusterDef()); if (hasClusterDef()) { result = result && getClusterDef() .equals(other.getClusterDef()); } result = result && (getIsolateSessionState() == other.getIsolateSessionState()); result = result && (hasExperimental() == other.hasExperimental()); if (hasExperimental()) { result = result && getExperimental() .equals(other.getExperimental()); } result = result && unknownFields.equals(other.unknownFields); return result; } @java.lang.Override public int hashCode() { if (memoizedHashCode != 0) { return memoizedHashCode; } int hash = 41; hash = (19 * hash) + getDescriptor().hashCode(); if (!internalGetDeviceCount().getMap().isEmpty()) { hash = (37 * hash) + DEVICE_COUNT_FIELD_NUMBER; hash = (53 * hash) + internalGetDeviceCount().hashCode(); } hash = (37 * hash) + INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER; hash = (53 * hash) + getIntraOpParallelismThreads(); hash = (37 * hash) + INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER; hash = (53 * hash) + getInterOpParallelismThreads(); hash = (37 * hash) + USE_PER_SESSION_THREADS_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean( getUsePerSessionThreads()); if (getSessionInterOpThreadPoolCount() > 0) { hash = (37 * hash) + SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER; hash = (53 * hash) + getSessionInterOpThreadPoolList().hashCode(); } hash = (37 * hash) + PLACEMENT_PERIOD_FIELD_NUMBER; hash = (53 * hash) + getPlacementPeriod(); if (getDeviceFiltersCount() > 0) { hash = (37 * hash) + DEVICE_FILTERS_FIELD_NUMBER; hash = (53 * hash) + getDeviceFiltersList().hashCode(); } if (hasGpuOptions()) { hash = (37 * hash) + GPU_OPTIONS_FIELD_NUMBER; hash = (53 * hash) + getGpuOptions().hashCode(); } hash = (37 * hash) + ALLOW_SOFT_PLACEMENT_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean( getAllowSoftPlacement()); hash = (37 * hash) + LOG_DEVICE_PLACEMENT_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean( getLogDevicePlacement()); if (hasGraphOptions()) { hash = (37 * hash) + GRAPH_OPTIONS_FIELD_NUMBER; hash = (53 * hash) + getGraphOptions().hashCode(); } hash = (37 * hash) + OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashLong( getOperationTimeoutInMs()); if (hasRpcOptions()) { hash = (37 * hash) + RPC_OPTIONS_FIELD_NUMBER; hash = (53 * hash) + getRpcOptions().hashCode(); } if (hasClusterDef()) { hash = (37 * hash) + CLUSTER_DEF_FIELD_NUMBER; hash = (53 * hash) + getClusterDef().hashCode(); } hash = (37 * hash) + ISOLATE_SESSION_STATE_FIELD_NUMBER; hash = (53 * hash) + org.nd4j.shade.protobuf.Internal.hashBoolean( getIsolateSessionState()); if (hasExperimental()) { hash = (37 * hash) + EXPERIMENTAL_FIELD_NUMBER; hash = (53 * hash) + getExperimental().hashCode(); } hash = (29 * hash) + unknownFields.hashCode(); memoizedHashCode = hash; return hash; } public static org.tensorflow.framework.ConfigProto parseFrom( java.nio.ByteBuffer data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto parseFrom( java.nio.ByteBuffer data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto parseFrom( org.nd4j.shade.protobuf.ByteString data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto parseFrom( org.nd4j.shade.protobuf.ByteString data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto parseFrom(byte[] data) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data); } public static org.tensorflow.framework.ConfigProto parseFrom( byte[] data, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return PARSER.parseFrom(data, extensionRegistry); } public static org.tensorflow.framework.ConfigProto parseFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto parseFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.ConfigProto parseDelimitedFrom(java.io.InputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto parseDelimitedFrom( java.io.InputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseDelimitedWithIOException(PARSER, input, extensionRegistry); } public static org.tensorflow.framework.ConfigProto parseFrom( org.nd4j.shade.protobuf.CodedInputStream input) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input); } public static org.tensorflow.framework.ConfigProto parseFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { return org.nd4j.shade.protobuf.GeneratedMessageV3 .parseWithIOException(PARSER, input, extensionRegistry); } public Builder newBuilderForType() { return newBuilder(); } public static Builder newBuilder() { return DEFAULT_INSTANCE.toBuilder(); } public static Builder newBuilder(org.tensorflow.framework.ConfigProto 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( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { Builder builder = new Builder(parent); return builder; } /** *
   * Session configuration parameters.
   * The system picks appropriate values for fields that are not set.
   * 
* * Protobuf type {@code tensorflow.ConfigProto} */ public static final class Builder extends org.nd4j.shade.protobuf.GeneratedMessageV3.Builder implements // @@protoc_insertion_point(builder_implements:tensorflow.ConfigProto) org.tensorflow.framework.ConfigProtoOrBuilder { public static final org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptor() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_descriptor; } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMapField( int number) { switch (number) { case 1: return internalGetDeviceCount(); default: throw new RuntimeException( "Invalid map field number: " + number); } } @SuppressWarnings({"rawtypes"}) protected org.nd4j.shade.protobuf.MapField internalGetMutableMapField( int number) { switch (number) { case 1: return internalGetMutableDeviceCount(); default: throw new RuntimeException( "Invalid map field number: " + number); } } protected org.nd4j.shade.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_fieldAccessorTable .ensureFieldAccessorsInitialized( org.tensorflow.framework.ConfigProto.class, org.tensorflow.framework.ConfigProto.Builder.class); } // Construct using org.tensorflow.framework.ConfigProto.newBuilder() private Builder() { maybeForceBuilderInitialization(); } private Builder( org.nd4j.shade.protobuf.GeneratedMessageV3.BuilderParent parent) { super(parent); maybeForceBuilderInitialization(); } private void maybeForceBuilderInitialization() { if (org.nd4j.shade.protobuf.GeneratedMessageV3 .alwaysUseFieldBuilders) { getSessionInterOpThreadPoolFieldBuilder(); } } public Builder clear() { super.clear(); internalGetMutableDeviceCount().clear(); intraOpParallelismThreads_ = 0; interOpParallelismThreads_ = 0; usePerSessionThreads_ = false; if (sessionInterOpThreadPoolBuilder_ == null) { sessionInterOpThreadPool_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00000010); } else { sessionInterOpThreadPoolBuilder_.clear(); } placementPeriod_ = 0; deviceFilters_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; bitField0_ = (bitField0_ & ~0x00000040); if (gpuOptionsBuilder_ == null) { gpuOptions_ = null; } else { gpuOptions_ = null; gpuOptionsBuilder_ = null; } allowSoftPlacement_ = false; logDevicePlacement_ = false; if (graphOptionsBuilder_ == null) { graphOptions_ = null; } else { graphOptions_ = null; graphOptionsBuilder_ = null; } operationTimeoutInMs_ = 0L; if (rpcOptionsBuilder_ == null) { rpcOptions_ = null; } else { rpcOptions_ = null; rpcOptionsBuilder_ = null; } if (clusterDefBuilder_ == null) { clusterDef_ = null; } else { clusterDef_ = null; clusterDefBuilder_ = null; } isolateSessionState_ = false; if (experimentalBuilder_ == null) { experimental_ = null; } else { experimental_ = null; experimentalBuilder_ = null; } return this; } public org.nd4j.shade.protobuf.Descriptors.Descriptor getDescriptorForType() { return org.tensorflow.framework.ConfigProtos.internal_static_tensorflow_ConfigProto_descriptor; } public org.tensorflow.framework.ConfigProto getDefaultInstanceForType() { return org.tensorflow.framework.ConfigProto.getDefaultInstance(); } public org.tensorflow.framework.ConfigProto build() { org.tensorflow.framework.ConfigProto result = buildPartial(); if (!result.isInitialized()) { throw newUninitializedMessageException(result); } return result; } public org.tensorflow.framework.ConfigProto buildPartial() { org.tensorflow.framework.ConfigProto result = new org.tensorflow.framework.ConfigProto(this); int from_bitField0_ = bitField0_; int to_bitField0_ = 0; result.deviceCount_ = internalGetDeviceCount(); result.deviceCount_.makeImmutable(); result.intraOpParallelismThreads_ = intraOpParallelismThreads_; result.interOpParallelismThreads_ = interOpParallelismThreads_; result.usePerSessionThreads_ = usePerSessionThreads_; if (sessionInterOpThreadPoolBuilder_ == null) { if (((bitField0_ & 0x00000010) == 0x00000010)) { sessionInterOpThreadPool_ = java.util.Collections.unmodifiableList(sessionInterOpThreadPool_); bitField0_ = (bitField0_ & ~0x00000010); } result.sessionInterOpThreadPool_ = sessionInterOpThreadPool_; } else { result.sessionInterOpThreadPool_ = sessionInterOpThreadPoolBuilder_.build(); } result.placementPeriod_ = placementPeriod_; if (((bitField0_ & 0x00000040) == 0x00000040)) { deviceFilters_ = deviceFilters_.getUnmodifiableView(); bitField0_ = (bitField0_ & ~0x00000040); } result.deviceFilters_ = deviceFilters_; if (gpuOptionsBuilder_ == null) { result.gpuOptions_ = gpuOptions_; } else { result.gpuOptions_ = gpuOptionsBuilder_.build(); } result.allowSoftPlacement_ = allowSoftPlacement_; result.logDevicePlacement_ = logDevicePlacement_; if (graphOptionsBuilder_ == null) { result.graphOptions_ = graphOptions_; } else { result.graphOptions_ = graphOptionsBuilder_.build(); } result.operationTimeoutInMs_ = operationTimeoutInMs_; if (rpcOptionsBuilder_ == null) { result.rpcOptions_ = rpcOptions_; } else { result.rpcOptions_ = rpcOptionsBuilder_.build(); } if (clusterDefBuilder_ == null) { result.clusterDef_ = clusterDef_; } else { result.clusterDef_ = clusterDefBuilder_.build(); } result.isolateSessionState_ = isolateSessionState_; if (experimentalBuilder_ == null) { result.experimental_ = experimental_; } else { result.experimental_ = experimentalBuilder_.build(); } result.bitField0_ = to_bitField0_; onBuilt(); return result; } public Builder clone() { return (Builder) super.clone(); } public Builder setField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.setField(field, value); } public Builder clearField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field) { return (Builder) super.clearField(field); } public Builder clearOneof( org.nd4j.shade.protobuf.Descriptors.OneofDescriptor oneof) { return (Builder) super.clearOneof(oneof); } public Builder setRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, int index, java.lang.Object value) { return (Builder) super.setRepeatedField(field, index, value); } public Builder addRepeatedField( org.nd4j.shade.protobuf.Descriptors.FieldDescriptor field, java.lang.Object value) { return (Builder) super.addRepeatedField(field, value); } public Builder mergeFrom(org.nd4j.shade.protobuf.Message other) { if (other instanceof org.tensorflow.framework.ConfigProto) { return mergeFrom((org.tensorflow.framework.ConfigProto)other); } else { super.mergeFrom(other); return this; } } public Builder mergeFrom(org.tensorflow.framework.ConfigProto other) { if (other == org.tensorflow.framework.ConfigProto.getDefaultInstance()) return this; internalGetMutableDeviceCount().mergeFrom( other.internalGetDeviceCount()); if (other.getIntraOpParallelismThreads() != 0) { setIntraOpParallelismThreads(other.getIntraOpParallelismThreads()); } if (other.getInterOpParallelismThreads() != 0) { setInterOpParallelismThreads(other.getInterOpParallelismThreads()); } if (other.getUsePerSessionThreads() != false) { setUsePerSessionThreads(other.getUsePerSessionThreads()); } if (sessionInterOpThreadPoolBuilder_ == null) { if (!other.sessionInterOpThreadPool_.isEmpty()) { if (sessionInterOpThreadPool_.isEmpty()) { sessionInterOpThreadPool_ = other.sessionInterOpThreadPool_; bitField0_ = (bitField0_ & ~0x00000010); } else { ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.addAll(other.sessionInterOpThreadPool_); } onChanged(); } } else { if (!other.sessionInterOpThreadPool_.isEmpty()) { if (sessionInterOpThreadPoolBuilder_.isEmpty()) { sessionInterOpThreadPoolBuilder_.dispose(); sessionInterOpThreadPoolBuilder_ = null; sessionInterOpThreadPool_ = other.sessionInterOpThreadPool_; bitField0_ = (bitField0_ & ~0x00000010); sessionInterOpThreadPoolBuilder_ = org.nd4j.shade.protobuf.GeneratedMessageV3.alwaysUseFieldBuilders ? getSessionInterOpThreadPoolFieldBuilder() : null; } else { sessionInterOpThreadPoolBuilder_.addAllMessages(other.sessionInterOpThreadPool_); } } } if (other.getPlacementPeriod() != 0) { setPlacementPeriod(other.getPlacementPeriod()); } if (!other.deviceFilters_.isEmpty()) { if (deviceFilters_.isEmpty()) { deviceFilters_ = other.deviceFilters_; bitField0_ = (bitField0_ & ~0x00000040); } else { ensureDeviceFiltersIsMutable(); deviceFilters_.addAll(other.deviceFilters_); } onChanged(); } if (other.hasGpuOptions()) { mergeGpuOptions(other.getGpuOptions()); } if (other.getAllowSoftPlacement() != false) { setAllowSoftPlacement(other.getAllowSoftPlacement()); } if (other.getLogDevicePlacement() != false) { setLogDevicePlacement(other.getLogDevicePlacement()); } if (other.hasGraphOptions()) { mergeGraphOptions(other.getGraphOptions()); } if (other.getOperationTimeoutInMs() != 0L) { setOperationTimeoutInMs(other.getOperationTimeoutInMs()); } if (other.hasRpcOptions()) { mergeRpcOptions(other.getRpcOptions()); } if (other.hasClusterDef()) { mergeClusterDef(other.getClusterDef()); } if (other.getIsolateSessionState() != false) { setIsolateSessionState(other.getIsolateSessionState()); } if (other.hasExperimental()) { mergeExperimental(other.getExperimental()); } this.mergeUnknownFields(other.unknownFields); onChanged(); return this; } public final boolean isInitialized() { return true; } public Builder mergeFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws java.io.IOException { org.tensorflow.framework.ConfigProto parsedMessage = null; try { parsedMessage = PARSER.parsePartialFrom(input, extensionRegistry); } catch (org.nd4j.shade.protobuf.InvalidProtocolBufferException e) { parsedMessage = (org.tensorflow.framework.ConfigProto) e.getUnfinishedMessage(); throw e.unwrapIOException(); } finally { if (parsedMessage != null) { mergeFrom(parsedMessage); } } return this; } private int bitField0_; private org.nd4j.shade.protobuf.MapField< java.lang.String, java.lang.Integer> deviceCount_; private org.nd4j.shade.protobuf.MapField internalGetDeviceCount() { if (deviceCount_ == null) { return org.nd4j.shade.protobuf.MapField.emptyMapField( DeviceCountDefaultEntryHolder.defaultEntry); } return deviceCount_; } private org.nd4j.shade.protobuf.MapField internalGetMutableDeviceCount() { onChanged();; if (deviceCount_ == null) { deviceCount_ = org.nd4j.shade.protobuf.MapField.newMapField( DeviceCountDefaultEntryHolder.defaultEntry); } if (!deviceCount_.isMutable()) { deviceCount_ = deviceCount_.copy(); } return deviceCount_; } public int getDeviceCountCount() { return internalGetDeviceCount().getMap().size(); } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public boolean containsDeviceCount( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } return internalGetDeviceCount().getMap().containsKey(key); } /** * Use {@link #getDeviceCountMap()} instead. */ @java.lang.Deprecated public java.util.Map getDeviceCount() { return getDeviceCountMap(); } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public java.util.Map getDeviceCountMap() { return internalGetDeviceCount().getMap(); } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public int getDeviceCountOrDefault( java.lang.String key, int defaultValue) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetDeviceCount().getMap(); return map.containsKey(key) ? map.get(key) : defaultValue; } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public int getDeviceCountOrThrow( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } java.util.Map map = internalGetDeviceCount().getMap(); if (!map.containsKey(key)) { throw new java.lang.IllegalArgumentException(); } return map.get(key); } public Builder clearDeviceCount() { internalGetMutableDeviceCount().getMutableMap() .clear(); return this; } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public Builder removeDeviceCount( java.lang.String key) { if (key == null) { throw new java.lang.NullPointerException(); } internalGetMutableDeviceCount().getMutableMap() .remove(key); return this; } /** * Use alternate mutation accessors instead. */ @java.lang.Deprecated public java.util.Map getMutableDeviceCount() { return internalGetMutableDeviceCount().getMutableMap(); } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public Builder putDeviceCount( java.lang.String key, int value) { if (key == null) { throw new java.lang.NullPointerException(); } internalGetMutableDeviceCount().getMutableMap() .put(key, value); return this; } /** *
     * Map from device type name (e.g., "CPU" or "GPU" ) to maximum
     * number of devices of that type to use.  If a particular device
     * type is not found in the map, the system picks an appropriate
     * number.
     * 
* * map<string, int32> device_count = 1; */ public Builder putAllDeviceCount( java.util.Map values) { internalGetMutableDeviceCount().getMutableMap() .putAll(values); return this; } private int intraOpParallelismThreads_ ; /** *
     * The execution of an individual op (for some op types) can be
     * parallelized on a pool of intra_op_parallelism_threads.
     * 0 means the system picks an appropriate number.
     * 
* * int32 intra_op_parallelism_threads = 2; */ public int getIntraOpParallelismThreads() { return intraOpParallelismThreads_; } /** *
     * The execution of an individual op (for some op types) can be
     * parallelized on a pool of intra_op_parallelism_threads.
     * 0 means the system picks an appropriate number.
     * 
* * int32 intra_op_parallelism_threads = 2; */ public Builder setIntraOpParallelismThreads(int value) { intraOpParallelismThreads_ = value; onChanged(); return this; } /** *
     * The execution of an individual op (for some op types) can be
     * parallelized on a pool of intra_op_parallelism_threads.
     * 0 means the system picks an appropriate number.
     * 
* * int32 intra_op_parallelism_threads = 2; */ public Builder clearIntraOpParallelismThreads() { intraOpParallelismThreads_ = 0; onChanged(); return this; } private int interOpParallelismThreads_ ; /** *
     * Nodes that perform blocking operations are enqueued on a pool of
     * inter_op_parallelism_threads available in each process.
     * 0 means the system picks an appropriate number.
     * Note that the first Session created in the process sets the
     * number of threads for all future sessions unless use_per_session_threads is
     * true or session_inter_op_thread_pool is configured.
     * 
* * int32 inter_op_parallelism_threads = 5; */ public int getInterOpParallelismThreads() { return interOpParallelismThreads_; } /** *
     * Nodes that perform blocking operations are enqueued on a pool of
     * inter_op_parallelism_threads available in each process.
     * 0 means the system picks an appropriate number.
     * Note that the first Session created in the process sets the
     * number of threads for all future sessions unless use_per_session_threads is
     * true or session_inter_op_thread_pool is configured.
     * 
* * int32 inter_op_parallelism_threads = 5; */ public Builder setInterOpParallelismThreads(int value) { interOpParallelismThreads_ = value; onChanged(); return this; } /** *
     * Nodes that perform blocking operations are enqueued on a pool of
     * inter_op_parallelism_threads available in each process.
     * 0 means the system picks an appropriate number.
     * Note that the first Session created in the process sets the
     * number of threads for all future sessions unless use_per_session_threads is
     * true or session_inter_op_thread_pool is configured.
     * 
* * int32 inter_op_parallelism_threads = 5; */ public Builder clearInterOpParallelismThreads() { interOpParallelismThreads_ = 0; onChanged(); return this; } private boolean usePerSessionThreads_ ; /** *
     * If true, use a new set of threads for this session rather than the global
     * pool of threads. Only supported by direct sessions.
     * If false, use the global threads created by the first session, or the
     * per-session thread pools configured by session_inter_op_thread_pool.
     * This option is deprecated. The same effect can be achieved by setting
     * session_inter_op_thread_pool to have one element, whose num_threads equals
     * inter_op_parallelism_threads.
     * 
* * bool use_per_session_threads = 9; */ public boolean getUsePerSessionThreads() { return usePerSessionThreads_; } /** *
     * If true, use a new set of threads for this session rather than the global
     * pool of threads. Only supported by direct sessions.
     * If false, use the global threads created by the first session, or the
     * per-session thread pools configured by session_inter_op_thread_pool.
     * This option is deprecated. The same effect can be achieved by setting
     * session_inter_op_thread_pool to have one element, whose num_threads equals
     * inter_op_parallelism_threads.
     * 
* * bool use_per_session_threads = 9; */ public Builder setUsePerSessionThreads(boolean value) { usePerSessionThreads_ = value; onChanged(); return this; } /** *
     * If true, use a new set of threads for this session rather than the global
     * pool of threads. Only supported by direct sessions.
     * If false, use the global threads created by the first session, or the
     * per-session thread pools configured by session_inter_op_thread_pool.
     * This option is deprecated. The same effect can be achieved by setting
     * session_inter_op_thread_pool to have one element, whose num_threads equals
     * inter_op_parallelism_threads.
     * 
* * bool use_per_session_threads = 9; */ public Builder clearUsePerSessionThreads() { usePerSessionThreads_ = false; onChanged(); return this; } private java.util.List sessionInterOpThreadPool_ = java.util.Collections.emptyList(); private void ensureSessionInterOpThreadPoolIsMutable() { if (!((bitField0_ & 0x00000010) == 0x00000010)) { sessionInterOpThreadPool_ = new java.util.ArrayList(sessionInterOpThreadPool_); bitField0_ |= 0x00000010; } } private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.ThreadPoolOptionProto, org.tensorflow.framework.ThreadPoolOptionProto.Builder, org.tensorflow.framework.ThreadPoolOptionProtoOrBuilder> sessionInterOpThreadPoolBuilder_; /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public java.util.List getSessionInterOpThreadPoolList() { if (sessionInterOpThreadPoolBuilder_ == null) { return java.util.Collections.unmodifiableList(sessionInterOpThreadPool_); } else { return sessionInterOpThreadPoolBuilder_.getMessageList(); } } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public int getSessionInterOpThreadPoolCount() { if (sessionInterOpThreadPoolBuilder_ == null) { return sessionInterOpThreadPool_.size(); } else { return sessionInterOpThreadPoolBuilder_.getCount(); } } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProto getSessionInterOpThreadPool(int index) { if (sessionInterOpThreadPoolBuilder_ == null) { return sessionInterOpThreadPool_.get(index); } else { return sessionInterOpThreadPoolBuilder_.getMessage(index); } } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder setSessionInterOpThreadPool( int index, org.tensorflow.framework.ThreadPoolOptionProto value) { if (sessionInterOpThreadPoolBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.set(index, value); onChanged(); } else { sessionInterOpThreadPoolBuilder_.setMessage(index, value); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder setSessionInterOpThreadPool( int index, org.tensorflow.framework.ThreadPoolOptionProto.Builder builderForValue) { if (sessionInterOpThreadPoolBuilder_ == null) { ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.set(index, builderForValue.build()); onChanged(); } else { sessionInterOpThreadPoolBuilder_.setMessage(index, builderForValue.build()); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder addSessionInterOpThreadPool(org.tensorflow.framework.ThreadPoolOptionProto value) { if (sessionInterOpThreadPoolBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.add(value); onChanged(); } else { sessionInterOpThreadPoolBuilder_.addMessage(value); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder addSessionInterOpThreadPool( int index, org.tensorflow.framework.ThreadPoolOptionProto value) { if (sessionInterOpThreadPoolBuilder_ == null) { if (value == null) { throw new NullPointerException(); } ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.add(index, value); onChanged(); } else { sessionInterOpThreadPoolBuilder_.addMessage(index, value); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder addSessionInterOpThreadPool( org.tensorflow.framework.ThreadPoolOptionProto.Builder builderForValue) { if (sessionInterOpThreadPoolBuilder_ == null) { ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.add(builderForValue.build()); onChanged(); } else { sessionInterOpThreadPoolBuilder_.addMessage(builderForValue.build()); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder addSessionInterOpThreadPool( int index, org.tensorflow.framework.ThreadPoolOptionProto.Builder builderForValue) { if (sessionInterOpThreadPoolBuilder_ == null) { ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.add(index, builderForValue.build()); onChanged(); } else { sessionInterOpThreadPoolBuilder_.addMessage(index, builderForValue.build()); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder addAllSessionInterOpThreadPool( java.lang.Iterable values) { if (sessionInterOpThreadPoolBuilder_ == null) { ensureSessionInterOpThreadPoolIsMutable(); org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll( values, sessionInterOpThreadPool_); onChanged(); } else { sessionInterOpThreadPoolBuilder_.addAllMessages(values); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder clearSessionInterOpThreadPool() { if (sessionInterOpThreadPoolBuilder_ == null) { sessionInterOpThreadPool_ = java.util.Collections.emptyList(); bitField0_ = (bitField0_ & ~0x00000010); onChanged(); } else { sessionInterOpThreadPoolBuilder_.clear(); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public Builder removeSessionInterOpThreadPool(int index) { if (sessionInterOpThreadPoolBuilder_ == null) { ensureSessionInterOpThreadPoolIsMutable(); sessionInterOpThreadPool_.remove(index); onChanged(); } else { sessionInterOpThreadPoolBuilder_.remove(index); } return this; } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProto.Builder getSessionInterOpThreadPoolBuilder( int index) { return getSessionInterOpThreadPoolFieldBuilder().getBuilder(index); } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder( int index) { if (sessionInterOpThreadPoolBuilder_ == null) { return sessionInterOpThreadPool_.get(index); } else { return sessionInterOpThreadPoolBuilder_.getMessageOrBuilder(index); } } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public java.util.List getSessionInterOpThreadPoolOrBuilderList() { if (sessionInterOpThreadPoolBuilder_ != null) { return sessionInterOpThreadPoolBuilder_.getMessageOrBuilderList(); } else { return java.util.Collections.unmodifiableList(sessionInterOpThreadPool_); } } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProto.Builder addSessionInterOpThreadPoolBuilder() { return getSessionInterOpThreadPoolFieldBuilder().addBuilder( org.tensorflow.framework.ThreadPoolOptionProto.getDefaultInstance()); } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public org.tensorflow.framework.ThreadPoolOptionProto.Builder addSessionInterOpThreadPoolBuilder( int index) { return getSessionInterOpThreadPoolFieldBuilder().addBuilder( index, org.tensorflow.framework.ThreadPoolOptionProto.getDefaultInstance()); } /** *
     * This option is experimental - it may be replaced with a different mechanism
     * in the future.
     * Configures session thread pools. If this is configured, then RunOptions for
     * a Run call can select the thread pool to use.
     * The intended use is for when some session invocations need to run in a
     * background pool limited to a small number of threads:
     * - For example, a session may be configured to have one large pool (for
     * regular compute) and one small pool (for periodic, low priority work);
     * using the small pool is currently the mechanism for limiting the inter-op
     * parallelism of the low priority work.  Note that it does not limit the
     * parallelism of work spawned by a single op kernel implementation.
     * - Using this setting is normally not needed in training, but may help some
     * serving use cases.
     * - It is also generally recommended to set the global_name field of this
     * proto, to avoid creating multiple large pools. It is typically better to
     * run the non-low-priority work, even across sessions, in a single large
     * pool.
     * 
* * repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12; */ public java.util.List getSessionInterOpThreadPoolBuilderList() { return getSessionInterOpThreadPoolFieldBuilder().getBuilderList(); } private org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.ThreadPoolOptionProto, org.tensorflow.framework.ThreadPoolOptionProto.Builder, org.tensorflow.framework.ThreadPoolOptionProtoOrBuilder> getSessionInterOpThreadPoolFieldBuilder() { if (sessionInterOpThreadPoolBuilder_ == null) { sessionInterOpThreadPoolBuilder_ = new org.nd4j.shade.protobuf.RepeatedFieldBuilderV3< org.tensorflow.framework.ThreadPoolOptionProto, org.tensorflow.framework.ThreadPoolOptionProto.Builder, org.tensorflow.framework.ThreadPoolOptionProtoOrBuilder>( sessionInterOpThreadPool_, ((bitField0_ & 0x00000010) == 0x00000010), getParentForChildren(), isClean()); sessionInterOpThreadPool_ = null; } return sessionInterOpThreadPoolBuilder_; } private int placementPeriod_ ; /** *
     * Assignment of Nodes to Devices is recomputed every placement_period
     * steps until the system warms up (at which point the recomputation
     * typically slows down automatically).
     * 
* * int32 placement_period = 3; */ public int getPlacementPeriod() { return placementPeriod_; } /** *
     * Assignment of Nodes to Devices is recomputed every placement_period
     * steps until the system warms up (at which point the recomputation
     * typically slows down automatically).
     * 
* * int32 placement_period = 3; */ public Builder setPlacementPeriod(int value) { placementPeriod_ = value; onChanged(); return this; } /** *
     * Assignment of Nodes to Devices is recomputed every placement_period
     * steps until the system warms up (at which point the recomputation
     * typically slows down automatically).
     * 
* * int32 placement_period = 3; */ public Builder clearPlacementPeriod() { placementPeriod_ = 0; onChanged(); return this; } private org.nd4j.shade.protobuf.LazyStringList deviceFilters_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; private void ensureDeviceFiltersIsMutable() { if (!((bitField0_ & 0x00000040) == 0x00000040)) { deviceFilters_ = new org.nd4j.shade.protobuf.LazyStringArrayList(deviceFilters_); bitField0_ |= 0x00000040; } } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public org.nd4j.shade.protobuf.ProtocolStringList getDeviceFiltersList() { return deviceFilters_.getUnmodifiableView(); } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public int getDeviceFiltersCount() { return deviceFilters_.size(); } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public java.lang.String getDeviceFilters(int index) { return deviceFilters_.get(index); } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public org.nd4j.shade.protobuf.ByteString getDeviceFiltersBytes(int index) { return deviceFilters_.getByteString(index); } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public Builder setDeviceFilters( int index, java.lang.String value) { if (value == null) { throw new NullPointerException(); } ensureDeviceFiltersIsMutable(); deviceFilters_.set(index, value); onChanged(); return this; } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public Builder addDeviceFilters( java.lang.String value) { if (value == null) { throw new NullPointerException(); } ensureDeviceFiltersIsMutable(); deviceFilters_.add(value); onChanged(); return this; } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public Builder addAllDeviceFilters( java.lang.Iterable values) { ensureDeviceFiltersIsMutable(); org.nd4j.shade.protobuf.AbstractMessageLite.Builder.addAll( values, deviceFilters_); onChanged(); return this; } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public Builder clearDeviceFilters() { deviceFilters_ = org.nd4j.shade.protobuf.LazyStringArrayList.EMPTY; bitField0_ = (bitField0_ & ~0x00000040); onChanged(); return this; } /** *
     * When any filters are present sessions will ignore all devices which do not
     * match the filters. Each filter can be partially specified, e.g. "/job:ps"
     * "/job:worker/replica:3", etc.
     * 
* * repeated string device_filters = 4; */ public Builder addDeviceFiltersBytes( org.nd4j.shade.protobuf.ByteString value) { if (value == null) { throw new NullPointerException(); } checkByteStringIsUtf8(value); ensureDeviceFiltersIsMutable(); deviceFilters_.add(value); onChanged(); return this; } private org.tensorflow.framework.GPUOptions gpuOptions_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GPUOptions, org.tensorflow.framework.GPUOptions.Builder, org.tensorflow.framework.GPUOptionsOrBuilder> gpuOptionsBuilder_; /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public boolean hasGpuOptions() { return gpuOptionsBuilder_ != null || gpuOptions_ != null; } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public org.tensorflow.framework.GPUOptions getGpuOptions() { if (gpuOptionsBuilder_ == null) { return gpuOptions_ == null ? org.tensorflow.framework.GPUOptions.getDefaultInstance() : gpuOptions_; } else { return gpuOptionsBuilder_.getMessage(); } } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public Builder setGpuOptions(org.tensorflow.framework.GPUOptions value) { if (gpuOptionsBuilder_ == null) { if (value == null) { throw new NullPointerException(); } gpuOptions_ = value; onChanged(); } else { gpuOptionsBuilder_.setMessage(value); } return this; } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public Builder setGpuOptions( org.tensorflow.framework.GPUOptions.Builder builderForValue) { if (gpuOptionsBuilder_ == null) { gpuOptions_ = builderForValue.build(); onChanged(); } else { gpuOptionsBuilder_.setMessage(builderForValue.build()); } return this; } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public Builder mergeGpuOptions(org.tensorflow.framework.GPUOptions value) { if (gpuOptionsBuilder_ == null) { if (gpuOptions_ != null) { gpuOptions_ = org.tensorflow.framework.GPUOptions.newBuilder(gpuOptions_).mergeFrom(value).buildPartial(); } else { gpuOptions_ = value; } onChanged(); } else { gpuOptionsBuilder_.mergeFrom(value); } return this; } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public Builder clearGpuOptions() { if (gpuOptionsBuilder_ == null) { gpuOptions_ = null; onChanged(); } else { gpuOptions_ = null; gpuOptionsBuilder_ = null; } return this; } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public org.tensorflow.framework.GPUOptions.Builder getGpuOptionsBuilder() { onChanged(); return getGpuOptionsFieldBuilder().getBuilder(); } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ public org.tensorflow.framework.GPUOptionsOrBuilder getGpuOptionsOrBuilder() { if (gpuOptionsBuilder_ != null) { return gpuOptionsBuilder_.getMessageOrBuilder(); } else { return gpuOptions_ == null ? org.tensorflow.framework.GPUOptions.getDefaultInstance() : gpuOptions_; } } /** *
     * Options that apply to all GPUs.
     * 
* * .tensorflow.GPUOptions gpu_options = 6; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GPUOptions, org.tensorflow.framework.GPUOptions.Builder, org.tensorflow.framework.GPUOptionsOrBuilder> getGpuOptionsFieldBuilder() { if (gpuOptionsBuilder_ == null) { gpuOptionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GPUOptions, org.tensorflow.framework.GPUOptions.Builder, org.tensorflow.framework.GPUOptionsOrBuilder>( getGpuOptions(), getParentForChildren(), isClean()); gpuOptions_ = null; } return gpuOptionsBuilder_; } private boolean allowSoftPlacement_ ; /** *
     * Whether soft placement is allowed. If allow_soft_placement is true,
     * an op will be placed on CPU if
     *   1. there's no GPU implementation for the OP
     * or
     *   2. no GPU devices are known or registered
     * or
     *   3. need to co-locate with reftype input(s) which are from CPU.
     * 
* * bool allow_soft_placement = 7; */ public boolean getAllowSoftPlacement() { return allowSoftPlacement_; } /** *
     * Whether soft placement is allowed. If allow_soft_placement is true,
     * an op will be placed on CPU if
     *   1. there's no GPU implementation for the OP
     * or
     *   2. no GPU devices are known or registered
     * or
     *   3. need to co-locate with reftype input(s) which are from CPU.
     * 
* * bool allow_soft_placement = 7; */ public Builder setAllowSoftPlacement(boolean value) { allowSoftPlacement_ = value; onChanged(); return this; } /** *
     * Whether soft placement is allowed. If allow_soft_placement is true,
     * an op will be placed on CPU if
     *   1. there's no GPU implementation for the OP
     * or
     *   2. no GPU devices are known or registered
     * or
     *   3. need to co-locate with reftype input(s) which are from CPU.
     * 
* * bool allow_soft_placement = 7; */ public Builder clearAllowSoftPlacement() { allowSoftPlacement_ = false; onChanged(); return this; } private boolean logDevicePlacement_ ; /** *
     * Whether device placements should be logged.
     * 
* * bool log_device_placement = 8; */ public boolean getLogDevicePlacement() { return logDevicePlacement_; } /** *
     * Whether device placements should be logged.
     * 
* * bool log_device_placement = 8; */ public Builder setLogDevicePlacement(boolean value) { logDevicePlacement_ = value; onChanged(); return this; } /** *
     * Whether device placements should be logged.
     * 
* * bool log_device_placement = 8; */ public Builder clearLogDevicePlacement() { logDevicePlacement_ = false; onChanged(); return this; } private org.tensorflow.framework.GraphOptions graphOptions_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GraphOptions, org.tensorflow.framework.GraphOptions.Builder, org.tensorflow.framework.GraphOptionsOrBuilder> graphOptionsBuilder_; /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public boolean hasGraphOptions() { return graphOptionsBuilder_ != null || graphOptions_ != null; } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public org.tensorflow.framework.GraphOptions getGraphOptions() { if (graphOptionsBuilder_ == null) { return graphOptions_ == null ? org.tensorflow.framework.GraphOptions.getDefaultInstance() : graphOptions_; } else { return graphOptionsBuilder_.getMessage(); } } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public Builder setGraphOptions(org.tensorflow.framework.GraphOptions value) { if (graphOptionsBuilder_ == null) { if (value == null) { throw new NullPointerException(); } graphOptions_ = value; onChanged(); } else { graphOptionsBuilder_.setMessage(value); } return this; } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public Builder setGraphOptions( org.tensorflow.framework.GraphOptions.Builder builderForValue) { if (graphOptionsBuilder_ == null) { graphOptions_ = builderForValue.build(); onChanged(); } else { graphOptionsBuilder_.setMessage(builderForValue.build()); } return this; } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public Builder mergeGraphOptions(org.tensorflow.framework.GraphOptions value) { if (graphOptionsBuilder_ == null) { if (graphOptions_ != null) { graphOptions_ = org.tensorflow.framework.GraphOptions.newBuilder(graphOptions_).mergeFrom(value).buildPartial(); } else { graphOptions_ = value; } onChanged(); } else { graphOptionsBuilder_.mergeFrom(value); } return this; } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public Builder clearGraphOptions() { if (graphOptionsBuilder_ == null) { graphOptions_ = null; onChanged(); } else { graphOptions_ = null; graphOptionsBuilder_ = null; } return this; } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public org.tensorflow.framework.GraphOptions.Builder getGraphOptionsBuilder() { onChanged(); return getGraphOptionsFieldBuilder().getBuilder(); } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ public org.tensorflow.framework.GraphOptionsOrBuilder getGraphOptionsOrBuilder() { if (graphOptionsBuilder_ != null) { return graphOptionsBuilder_.getMessageOrBuilder(); } else { return graphOptions_ == null ? org.tensorflow.framework.GraphOptions.getDefaultInstance() : graphOptions_; } } /** *
     * Options that apply to all graphs.
     * 
* * .tensorflow.GraphOptions graph_options = 10; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GraphOptions, org.tensorflow.framework.GraphOptions.Builder, org.tensorflow.framework.GraphOptionsOrBuilder> getGraphOptionsFieldBuilder() { if (graphOptionsBuilder_ == null) { graphOptionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.GraphOptions, org.tensorflow.framework.GraphOptions.Builder, org.tensorflow.framework.GraphOptionsOrBuilder>( getGraphOptions(), getParentForChildren(), isClean()); graphOptions_ = null; } return graphOptionsBuilder_; } private long operationTimeoutInMs_ ; /** *
     * Global timeout for all blocking operations in this session.  If non-zero,
     * and not overridden on a per-operation basis, this value will be used as the
     * deadline for all blocking operations.
     * 
* * int64 operation_timeout_in_ms = 11; */ public long getOperationTimeoutInMs() { return operationTimeoutInMs_; } /** *
     * Global timeout for all blocking operations in this session.  If non-zero,
     * and not overridden on a per-operation basis, this value will be used as the
     * deadline for all blocking operations.
     * 
* * int64 operation_timeout_in_ms = 11; */ public Builder setOperationTimeoutInMs(long value) { operationTimeoutInMs_ = value; onChanged(); return this; } /** *
     * Global timeout for all blocking operations in this session.  If non-zero,
     * and not overridden on a per-operation basis, this value will be used as the
     * deadline for all blocking operations.
     * 
* * int64 operation_timeout_in_ms = 11; */ public Builder clearOperationTimeoutInMs() { operationTimeoutInMs_ = 0L; onChanged(); return this; } private org.tensorflow.framework.RPCOptions rpcOptions_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.RPCOptions, org.tensorflow.framework.RPCOptions.Builder, org.tensorflow.framework.RPCOptionsOrBuilder> rpcOptionsBuilder_; /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public boolean hasRpcOptions() { return rpcOptionsBuilder_ != null || rpcOptions_ != null; } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public org.tensorflow.framework.RPCOptions getRpcOptions() { if (rpcOptionsBuilder_ == null) { return rpcOptions_ == null ? org.tensorflow.framework.RPCOptions.getDefaultInstance() : rpcOptions_; } else { return rpcOptionsBuilder_.getMessage(); } } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public Builder setRpcOptions(org.tensorflow.framework.RPCOptions value) { if (rpcOptionsBuilder_ == null) { if (value == null) { throw new NullPointerException(); } rpcOptions_ = value; onChanged(); } else { rpcOptionsBuilder_.setMessage(value); } return this; } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public Builder setRpcOptions( org.tensorflow.framework.RPCOptions.Builder builderForValue) { if (rpcOptionsBuilder_ == null) { rpcOptions_ = builderForValue.build(); onChanged(); } else { rpcOptionsBuilder_.setMessage(builderForValue.build()); } return this; } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public Builder mergeRpcOptions(org.tensorflow.framework.RPCOptions value) { if (rpcOptionsBuilder_ == null) { if (rpcOptions_ != null) { rpcOptions_ = org.tensorflow.framework.RPCOptions.newBuilder(rpcOptions_).mergeFrom(value).buildPartial(); } else { rpcOptions_ = value; } onChanged(); } else { rpcOptionsBuilder_.mergeFrom(value); } return this; } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public Builder clearRpcOptions() { if (rpcOptionsBuilder_ == null) { rpcOptions_ = null; onChanged(); } else { rpcOptions_ = null; rpcOptionsBuilder_ = null; } return this; } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public org.tensorflow.framework.RPCOptions.Builder getRpcOptionsBuilder() { onChanged(); return getRpcOptionsFieldBuilder().getBuilder(); } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ public org.tensorflow.framework.RPCOptionsOrBuilder getRpcOptionsOrBuilder() { if (rpcOptionsBuilder_ != null) { return rpcOptionsBuilder_.getMessageOrBuilder(); } else { return rpcOptions_ == null ? org.tensorflow.framework.RPCOptions.getDefaultInstance() : rpcOptions_; } } /** *
     * Options that apply when this session uses the distributed runtime.
     * 
* * .tensorflow.RPCOptions rpc_options = 13; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.RPCOptions, org.tensorflow.framework.RPCOptions.Builder, org.tensorflow.framework.RPCOptionsOrBuilder> getRpcOptionsFieldBuilder() { if (rpcOptionsBuilder_ == null) { rpcOptionsBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.RPCOptions, org.tensorflow.framework.RPCOptions.Builder, org.tensorflow.framework.RPCOptionsOrBuilder>( getRpcOptions(), getParentForChildren(), isClean()); rpcOptions_ = null; } return rpcOptionsBuilder_; } private org.tensorflow.distruntime.ClusterDef clusterDef_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.distruntime.ClusterDef, org.tensorflow.distruntime.ClusterDef.Builder, org.tensorflow.distruntime.ClusterDefOrBuilder> clusterDefBuilder_; /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public boolean hasClusterDef() { return clusterDefBuilder_ != null || clusterDef_ != null; } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public org.tensorflow.distruntime.ClusterDef getClusterDef() { if (clusterDefBuilder_ == null) { return clusterDef_ == null ? org.tensorflow.distruntime.ClusterDef.getDefaultInstance() : clusterDef_; } else { return clusterDefBuilder_.getMessage(); } } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public Builder setClusterDef(org.tensorflow.distruntime.ClusterDef value) { if (clusterDefBuilder_ == null) { if (value == null) { throw new NullPointerException(); } clusterDef_ = value; onChanged(); } else { clusterDefBuilder_.setMessage(value); } return this; } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public Builder setClusterDef( org.tensorflow.distruntime.ClusterDef.Builder builderForValue) { if (clusterDefBuilder_ == null) { clusterDef_ = builderForValue.build(); onChanged(); } else { clusterDefBuilder_.setMessage(builderForValue.build()); } return this; } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public Builder mergeClusterDef(org.tensorflow.distruntime.ClusterDef value) { if (clusterDefBuilder_ == null) { if (clusterDef_ != null) { clusterDef_ = org.tensorflow.distruntime.ClusterDef.newBuilder(clusterDef_).mergeFrom(value).buildPartial(); } else { clusterDef_ = value; } onChanged(); } else { clusterDefBuilder_.mergeFrom(value); } return this; } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public Builder clearClusterDef() { if (clusterDefBuilder_ == null) { clusterDef_ = null; onChanged(); } else { clusterDef_ = null; clusterDefBuilder_ = null; } return this; } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public org.tensorflow.distruntime.ClusterDef.Builder getClusterDefBuilder() { onChanged(); return getClusterDefFieldBuilder().getBuilder(); } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ public org.tensorflow.distruntime.ClusterDefOrBuilder getClusterDefOrBuilder() { if (clusterDefBuilder_ != null) { return clusterDefBuilder_.getMessageOrBuilder(); } else { return clusterDef_ == null ? org.tensorflow.distruntime.ClusterDef.getDefaultInstance() : clusterDef_; } } /** *
     * Optional list of all workers to use in this session.
     * 
* * .tensorflow.ClusterDef cluster_def = 14; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.distruntime.ClusterDef, org.tensorflow.distruntime.ClusterDef.Builder, org.tensorflow.distruntime.ClusterDefOrBuilder> getClusterDefFieldBuilder() { if (clusterDefBuilder_ == null) { clusterDefBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.distruntime.ClusterDef, org.tensorflow.distruntime.ClusterDef.Builder, org.tensorflow.distruntime.ClusterDefOrBuilder>( getClusterDef(), getParentForChildren(), isClean()); clusterDef_ = null; } return clusterDefBuilder_; } private boolean isolateSessionState_ ; /** *
     * If true, any resources such as Variables used in the session will not be
     * shared with other sessions.
     * 
* * bool isolate_session_state = 15; */ public boolean getIsolateSessionState() { return isolateSessionState_; } /** *
     * If true, any resources such as Variables used in the session will not be
     * shared with other sessions.
     * 
* * bool isolate_session_state = 15; */ public Builder setIsolateSessionState(boolean value) { isolateSessionState_ = value; onChanged(); return this; } /** *
     * If true, any resources such as Variables used in the session will not be
     * shared with other sessions.
     * 
* * bool isolate_session_state = 15; */ public Builder clearIsolateSessionState() { isolateSessionState_ = false; onChanged(); return this; } private org.tensorflow.framework.ConfigProto.Experimental experimental_ = null; private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ConfigProto.Experimental, org.tensorflow.framework.ConfigProto.Experimental.Builder, org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder> experimentalBuilder_; /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public boolean hasExperimental() { return experimentalBuilder_ != null || experimental_ != null; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public org.tensorflow.framework.ConfigProto.Experimental getExperimental() { if (experimentalBuilder_ == null) { return experimental_ == null ? org.tensorflow.framework.ConfigProto.Experimental.getDefaultInstance() : experimental_; } else { return experimentalBuilder_.getMessage(); } } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public Builder setExperimental(org.tensorflow.framework.ConfigProto.Experimental value) { if (experimentalBuilder_ == null) { if (value == null) { throw new NullPointerException(); } experimental_ = value; onChanged(); } else { experimentalBuilder_.setMessage(value); } return this; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public Builder setExperimental( org.tensorflow.framework.ConfigProto.Experimental.Builder builderForValue) { if (experimentalBuilder_ == null) { experimental_ = builderForValue.build(); onChanged(); } else { experimentalBuilder_.setMessage(builderForValue.build()); } return this; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public Builder mergeExperimental(org.tensorflow.framework.ConfigProto.Experimental value) { if (experimentalBuilder_ == null) { if (experimental_ != null) { experimental_ = org.tensorflow.framework.ConfigProto.Experimental.newBuilder(experimental_).mergeFrom(value).buildPartial(); } else { experimental_ = value; } onChanged(); } else { experimentalBuilder_.mergeFrom(value); } return this; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public Builder clearExperimental() { if (experimentalBuilder_ == null) { experimental_ = null; onChanged(); } else { experimental_ = null; experimentalBuilder_ = null; } return this; } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public org.tensorflow.framework.ConfigProto.Experimental.Builder getExperimentalBuilder() { onChanged(); return getExperimentalFieldBuilder().getBuilder(); } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ public org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder() { if (experimentalBuilder_ != null) { return experimentalBuilder_.getMessageOrBuilder(); } else { return experimental_ == null ? org.tensorflow.framework.ConfigProto.Experimental.getDefaultInstance() : experimental_; } } /** * .tensorflow.ConfigProto.Experimental experimental = 16; */ private org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ConfigProto.Experimental, org.tensorflow.framework.ConfigProto.Experimental.Builder, org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder> getExperimentalFieldBuilder() { if (experimentalBuilder_ == null) { experimentalBuilder_ = new org.nd4j.shade.protobuf.SingleFieldBuilderV3< org.tensorflow.framework.ConfigProto.Experimental, org.tensorflow.framework.ConfigProto.Experimental.Builder, org.tensorflow.framework.ConfigProto.ExperimentalOrBuilder>( getExperimental(), getParentForChildren(), isClean()); experimental_ = null; } return experimentalBuilder_; } public final Builder setUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.setUnknownFieldsProto3(unknownFields); } public final Builder mergeUnknownFields( final org.nd4j.shade.protobuf.UnknownFieldSet unknownFields) { return super.mergeUnknownFields(unknownFields); } // @@protoc_insertion_point(builder_scope:tensorflow.ConfigProto) } // @@protoc_insertion_point(class_scope:tensorflow.ConfigProto) private static final org.tensorflow.framework.ConfigProto DEFAULT_INSTANCE; static { DEFAULT_INSTANCE = new org.tensorflow.framework.ConfigProto(); } public static org.tensorflow.framework.ConfigProto getDefaultInstance() { return DEFAULT_INSTANCE; } private static final org.nd4j.shade.protobuf.Parser PARSER = new org.nd4j.shade.protobuf.AbstractParser() { public ConfigProto parsePartialFrom( org.nd4j.shade.protobuf.CodedInputStream input, org.nd4j.shade.protobuf.ExtensionRegistryLite extensionRegistry) throws org.nd4j.shade.protobuf.InvalidProtocolBufferException { return new ConfigProto(input, extensionRegistry); } }; public static org.nd4j.shade.protobuf.Parser parser() { return PARSER; } @java.lang.Override public org.nd4j.shade.protobuf.Parser getParserForType() { return PARSER; } public org.tensorflow.framework.ConfigProto getDefaultInstanceForType() { return DEFAULT_INSTANCE; } }




© 2015 - 2024 Weber Informatics LLC | Privacy Policy