ConfigProto.Builder

public static final class ConfigProto.Builder

 Session configuration parameters.
 The system picks appropriate values for fields that are not set.
 
Protobuf type tensorflow.ConfigProto

Public Methods

ConfigProto.Builder
addAllDeviceFilters(Iterable<String> values)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addAllSessionInterOpThreadPool(Iterable<? extends ThreadPoolOptionProto> values)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addDeviceFilters(String value)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addDeviceFiltersBytes(com.google.protobuf.ByteString value)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
ConfigProto.Builder
addSessionInterOpThreadPool(ThreadPoolOptionProto value)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool(int index, ThreadPoolOptionProto value)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool(ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool(int index, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
addSessionInterOpThreadPoolBuilder(int index)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
addSessionInterOpThreadPoolBuilder()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto
build()
ConfigProto
ConfigProto.Builder
clear()
ConfigProto.Builder
clearAllowSoftPlacement()
 Whether soft placement is allowed.
ConfigProto.Builder
clearClusterDef()
 Optional list of all workers to use in this session.
ConfigProto.Builder
ConfigProto.Builder
clearDeviceFilters()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
clearExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
clearField(com.google.protobuf.Descriptors.FieldDescriptor field)
ConfigProto.Builder
clearGpuOptions()
 Options that apply to all GPUs.
ConfigProto.Builder
clearGraphOptions()
 Options that apply to all graphs.
ConfigProto.Builder
clearInterOpParallelismThreads()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ConfigProto.Builder
clearIntraOpParallelismThreads()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
ConfigProto.Builder
clearIsolateSessionState()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
ConfigProto.Builder
clearLogDevicePlacement()
 Whether device placements should be logged.
ConfigProto.Builder
clearOneof(com.google.protobuf.Descriptors.OneofDescriptor oneof)
ConfigProto.Builder
clearOperationTimeoutInMs()
 Global timeout for all blocking operations in this session.
ConfigProto.Builder
clearPlacementPeriod()
 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).
ConfigProto.Builder
clearRpcOptions()
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
clearSessionInterOpThreadPool()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
clearShareClusterDevicesInSession()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
ConfigProto.Builder
clearUsePerSessionThreads()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
ConfigProto.Builder
clone()
boolean
containsDeviceCount(String key)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
boolean
getAllowSoftPlacement()
 Whether soft placement is allowed.
ClusterDef
getClusterDef()
 Optional list of all workers to use in this session.
ClusterDef.Builder
getClusterDefBuilder()
 Optional list of all workers to use in this session.
ClusterDefOrBuilder
getClusterDefOrBuilder()
 Optional list of all workers to use in this session.
ConfigProto
final static com.google.protobuf.Descriptors.Descriptor
com.google.protobuf.Descriptors.Descriptor
Map<String, Integer>
int
getDeviceCountCount()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Map<String, Integer>
getDeviceCountMap()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrDefault(String key, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrThrow(String key)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
String
getDeviceFilters(int index)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ByteString
getDeviceFiltersBytes(int index)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
int
getDeviceFiltersCount()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ProtocolStringList
getDeviceFiltersList()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Experimental
getExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Experimental.Builder
getExperimentalBuilder()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder()
.tensorflow.ConfigProto.Experimental experimental = 16;
GPUOptions
getGpuOptions()
 Options that apply to all GPUs.
GPUOptions.Builder
getGpuOptionsBuilder()
 Options that apply to all GPUs.
GPUOptionsOrBuilder
getGpuOptionsOrBuilder()
 Options that apply to all GPUs.
GraphOptions
getGraphOptions()
 Options that apply to all graphs.
GraphOptions.Builder
getGraphOptionsBuilder()
 Options that apply to all graphs.
GraphOptionsOrBuilder
getGraphOptionsOrBuilder()
 Options that apply to all graphs.
int
getInterOpParallelismThreads()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
int
getIntraOpParallelismThreads()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
boolean
getIsolateSessionState()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
boolean
getLogDevicePlacement()
 Whether device placements should be logged.
Map<String, Integer>
getMutableDeviceCount()
Use alternate mutation accessors instead.
long
getOperationTimeoutInMs()
 Global timeout for all blocking operations in this session.
int
getPlacementPeriod()
 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).
RPCOptions
getRpcOptions()
 Options that apply when this session uses the distributed runtime.
RPCOptions.Builder
getRpcOptionsBuilder()
 Options that apply when this session uses the distributed runtime.
RPCOptionsOrBuilder
getRpcOptionsOrBuilder()
 Options that apply when this session uses the distributed runtime.
ThreadPoolOptionProto
getSessionInterOpThreadPool(int index)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
getSessionInterOpThreadPoolBuilder(int index)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
List<ThreadPoolOptionProto.Builder>
getSessionInterOpThreadPoolBuilderList()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
int
getSessionInterOpThreadPoolCount()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
List<ThreadPoolOptionProto>
getSessionInterOpThreadPoolList()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder(int index)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
List<? extends ThreadPoolOptionProtoOrBuilder>
getSessionInterOpThreadPoolOrBuilderList()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
boolean
getShareClusterDevicesInSession()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
boolean
getUsePerSessionThreads()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
boolean
hasClusterDef()
 Optional list of all workers to use in this session.
boolean
hasExperimental()
.tensorflow.ConfigProto.Experimental experimental = 16;
boolean
hasGpuOptions()
 Options that apply to all GPUs.
boolean
hasGraphOptions()
 Options that apply to all graphs.
boolean
hasRpcOptions()
 Options that apply when this session uses the distributed runtime.
final boolean
ConfigProto.Builder
mergeClusterDef(ClusterDef value)
 Optional list of all workers to use in this session.
ConfigProto.Builder
mergeExperimental(ConfigProto.Experimental value)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
mergeFrom(com.google.protobuf.Message other)
ConfigProto.Builder
mergeFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
ConfigProto.Builder
mergeGpuOptions(GPUOptions value)
 Options that apply to all GPUs.
ConfigProto.Builder
mergeGraphOptions(GraphOptions value)
 Options that apply to all graphs.
ConfigProto.Builder
mergeRpcOptions(RPCOptions value)
 Options that apply when this session uses the distributed runtime.
final ConfigProto.Builder
mergeUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
ConfigProto.Builder
putAllDeviceCount(Map<String, Integer> values)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
putDeviceCount(String key, int value)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
removeDeviceCount(String key)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
removeSessionInterOpThreadPool(int index)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setAllowSoftPlacement(boolean value)
 Whether soft placement is allowed.
ConfigProto.Builder
setClusterDef(ClusterDef.Builder builderForValue)
 Optional list of all workers to use in this session.
ConfigProto.Builder
setClusterDef(ClusterDef value)
 Optional list of all workers to use in this session.
ConfigProto.Builder
setDeviceFilters(int index, String value)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
setExperimental(ConfigProto.Experimental value)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
setExperimental(ConfigProto.Experimental.Builder builderForValue)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
setField(com.google.protobuf.Descriptors.FieldDescriptor field, Object value)
ConfigProto.Builder
setGpuOptions(GPUOptions.Builder builderForValue)
 Options that apply to all GPUs.
ConfigProto.Builder
setGpuOptions(GPUOptions value)
 Options that apply to all GPUs.
ConfigProto.Builder
setGraphOptions(GraphOptions.Builder builderForValue)
 Options that apply to all graphs.
ConfigProto.Builder
setGraphOptions(GraphOptions value)
 Options that apply to all graphs.
ConfigProto.Builder
setInterOpParallelismThreads(int value)
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ConfigProto.Builder
setIntraOpParallelismThreads(int value)
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
ConfigProto.Builder
setIsolateSessionState(boolean value)
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
ConfigProto.Builder
setLogDevicePlacement(boolean value)
 Whether device placements should be logged.
ConfigProto.Builder
setOperationTimeoutInMs(long value)
 Global timeout for all blocking operations in this session.
ConfigProto.Builder
setPlacementPeriod(int value)
 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).
ConfigProto.Builder
setRepeatedField(com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)
ConfigProto.Builder
setRpcOptions(RPCOptions value)
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
setRpcOptions(RPCOptions.Builder builderForValue)
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
setSessionInterOpThreadPool(int index, ThreadPoolOptionProto value)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setSessionInterOpThreadPool(int index, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setShareClusterDevicesInSession(boolean value)
 When true, WorkerSessions are created with device attributes from the
 full cluster.
final ConfigProto.Builder
setUnknownFields(com.google.protobuf.UnknownFieldSet unknownFields)
ConfigProto.Builder
setUsePerSessionThreads(boolean value)
 If true, use a new set of threads for this session rather than the global
 pool of threads.

Inherited Methods

Public Methods

public ConfigProto.Builder addAllDeviceFilters (Iterable<String> values)

 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 ConfigProto.Builder addAllSessionInterOpThreadPool (Iterable<? extends ThreadPoolOptionProto> values)

 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 ConfigProto.Builder addDeviceFilters (String value)

 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 ConfigProto.Builder addDeviceFiltersBytes (com.google.protobuf.ByteString value)

 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 ConfigProto.Builder addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, Object value)

public ConfigProto.Builder addSessionInterOpThreadPool (ThreadPoolOptionProto value)

 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 ConfigProto.Builder addSessionInterOpThreadPool (int index, ThreadPoolOptionProto value)

 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 ConfigProto.Builder addSessionInterOpThreadPool (ThreadPoolOptionProto.Builder builderForValue)

 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 ConfigProto.Builder addSessionInterOpThreadPool (int index, ThreadPoolOptionProto.Builder builderForValue)

 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 ThreadPoolOptionProto.Builder addSessionInterOpThreadPoolBuilder (int 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 ThreadPoolOptionProto.Builder addSessionInterOpThreadPoolBuilder ()

 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 ConfigProto build ()

public ConfigProto buildPartial ()

public ConfigProto.Builder clear ()

public ConfigProto.Builder clearAllowSoftPlacement ()

 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 ConfigProto.Builder clearClusterDef ()

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ConfigProto.Builder clearDeviceCount ()

public ConfigProto.Builder clearDeviceFilters ()

 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 ConfigProto.Builder clearExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Builder clearField (com.google.protobuf.Descriptors.FieldDescriptor field)

public ConfigProto.Builder clearGpuOptions ()

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public ConfigProto.Builder clearGraphOptions ()

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public ConfigProto.Builder clearInterOpParallelismThreads ()

 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.
 Negative means all operations are performed in caller's thread.
 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 ConfigProto.Builder clearIntraOpParallelismThreads ()

 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.
 If you create an ordinary session, e.g., from Python or C++,
 then there is exactly one intra op thread pool per process.
 The first session created determines the number of threads in this pool.
 All subsequent sessions reuse/share this one global pool.
 There are notable exceptions to the default behavior describe above:
 1. There is an environment variable  for overriding this thread pool,
    named TF_OVERRIDE_GLOBAL_THREADPOOL.
 2. When connecting to a server, such as a remote `tf.train.Server`
    instance, then this option will be ignored altogether.
 
int32 intra_op_parallelism_threads = 2;

public ConfigProto.Builder clearIsolateSessionState ()

 If true, any resources such as Variables used in the session will not be
 shared with other sessions. However, when clusterspec propagation is
 enabled, this field is ignored and sessions are always isolated.
 
bool isolate_session_state = 15;

public ConfigProto.Builder clearLogDevicePlacement ()

 Whether device placements should be logged.
 
bool log_device_placement = 8;

public ConfigProto.Builder clearOneof (com.google.protobuf.Descriptors.OneofDescriptor oneof)

public ConfigProto.Builder clearOperationTimeoutInMs ()

 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 ConfigProto.Builder clearPlacementPeriod ()

 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 ConfigProto.Builder clearRpcOptions ()

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public ConfigProto.Builder clearSessionInterOpThreadPool ()

 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 ConfigProto.Builder clearShareClusterDevicesInSession ()

 When true, WorkerSessions are created with device attributes from the
 full cluster.
 This is helpful when a worker wants to partition a graph
 (for example during a PartitionedCallOp).
 
bool share_cluster_devices_in_session = 17;

public ConfigProto.Builder clearUsePerSessionThreads ()

 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 ConfigProto.Builder clone ()

public boolean containsDeviceCount (String key)

 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 getAllowSoftPlacement ()

 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 ClusterDef getClusterDef ()

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ClusterDef.Builder getClusterDefBuilder ()

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ClusterDefOrBuilder getClusterDefOrBuilder ()

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ConfigProto getDefaultInstanceForType ()

public static final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

public com.google.protobuf.Descriptors.Descriptor getDescriptorForType ()

public Map<String, Integer> getDeviceCount ()

Use getDeviceCountMap() instead.

public int getDeviceCountCount ()

 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 Map<String, Integer> 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 int getDeviceCountOrDefault (String key, int 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 (String key)

 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 String getDeviceFilters (int 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 com.google.protobuf.ByteString getDeviceFiltersBytes (int 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 int getDeviceFiltersCount ()

 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 com.google.protobuf.ProtocolStringList getDeviceFiltersList ()

 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 ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Experimental.Builder getExperimentalBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public GPUOptions getGpuOptions ()

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public GPUOptions.Builder getGpuOptionsBuilder ()

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public GPUOptionsOrBuilder getGpuOptionsOrBuilder ()

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public GraphOptions getGraphOptions ()

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public GraphOptions.Builder getGraphOptionsBuilder ()

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public GraphOptionsOrBuilder getGraphOptionsOrBuilder ()

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public int getInterOpParallelismThreads ()

 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.
 Negative means all operations are performed in caller's thread.
 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 getIntraOpParallelismThreads ()

 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.
 If you create an ordinary session, e.g., from Python or C++,
 then there is exactly one intra op thread pool per process.
 The first session created determines the number of threads in this pool.
 All subsequent sessions reuse/share this one global pool.
 There are notable exceptions to the default behavior describe above:
 1. There is an environment variable  for overriding this thread pool,
    named TF_OVERRIDE_GLOBAL_THREADPOOL.
 2. When connecting to a server, such as a remote `tf.train.Server`
    instance, then this option will be ignored altogether.
 
int32 intra_op_parallelism_threads = 2;

public boolean getIsolateSessionState ()

 If true, any resources such as Variables used in the session will not be
 shared with other sessions. However, when clusterspec propagation is
 enabled, this field is ignored and sessions are always isolated.
 
bool isolate_session_state = 15;

public boolean getLogDevicePlacement ()

 Whether device placements should be logged.
 
bool log_device_placement = 8;

public Map<String, Integer> getMutableDeviceCount ()

Use alternate mutation accessors instead.

public long getOperationTimeoutInMs ()

 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 int getPlacementPeriod ()

 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 RPCOptions getRpcOptions ()

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public RPCOptions.Builder getRpcOptionsBuilder ()

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public RPCOptionsOrBuilder getRpcOptionsOrBuilder ()

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public ThreadPoolOptionProto getSessionInterOpThreadPool (int 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 ThreadPoolOptionProto.Builder getSessionInterOpThreadPoolBuilder (int 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 List<ThreadPoolOptionProto.Builder> getSessionInterOpThreadPoolBuilderList ()

 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 ()

 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 List<ThreadPoolOptionProto> getSessionInterOpThreadPoolList ()

 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 ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (int 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 List<? extends ThreadPoolOptionProtoOrBuilder> getSessionInterOpThreadPoolOrBuilderList ()

 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 boolean getShareClusterDevicesInSession ()

 When true, WorkerSessions are created with device attributes from the
 full cluster.
 This is helpful when a worker wants to partition a graph
 (for example during a PartitionedCallOp).
 
bool share_cluster_devices_in_session = 17;

public boolean getUsePerSessionThreads ()

 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 hasClusterDef ()

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public boolean hasExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public boolean hasGpuOptions ()

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public boolean hasGraphOptions ()

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public boolean hasRpcOptions ()

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public final boolean isInitialized ()

public ConfigProto.Builder mergeClusterDef (ClusterDef value)

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ConfigProto.Builder mergeExperimental (ConfigProto.Experimental value)

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Builder mergeFrom (com.google.protobuf.Message other)

public ConfigProto.Builder mergeFrom (com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Throws
IOException

public ConfigProto.Builder mergeGpuOptions (GPUOptions value)

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public ConfigProto.Builder mergeGraphOptions (GraphOptions value)

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public ConfigProto.Builder mergeRpcOptions (RPCOptions value)

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public final ConfigProto.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)

public ConfigProto.Builder putAllDeviceCount (Map<String, Integer> values)

 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 ConfigProto.Builder putDeviceCount (String key, int value)

 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 ConfigProto.Builder removeDeviceCount (String key)

 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 ConfigProto.Builder removeSessionInterOpThreadPool (int 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 ConfigProto.Builder setAllowSoftPlacement (boolean value)

 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 ConfigProto.Builder setClusterDef (ClusterDef.Builder builderForValue)

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ConfigProto.Builder setClusterDef (ClusterDef value)

 Optional list of all workers to use in this session.
 
.tensorflow.ClusterDef cluster_def = 14;

public ConfigProto.Builder setDeviceFilters (int index, String value)

 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 ConfigProto.Builder setExperimental (ConfigProto.Experimental value)

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Builder setExperimental (ConfigProto.Experimental.Builder builderForValue)

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Builder setField (com.google.protobuf.Descriptors.FieldDescriptor field, Object value)

public ConfigProto.Builder setGpuOptions (GPUOptions.Builder builderForValue)

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public ConfigProto.Builder setGpuOptions (GPUOptions value)

 Options that apply to all GPUs.
 
.tensorflow.GPUOptions gpu_options = 6;

public ConfigProto.Builder setGraphOptions (GraphOptions.Builder builderForValue)

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public ConfigProto.Builder setGraphOptions (GraphOptions value)

 Options that apply to all graphs.
 
.tensorflow.GraphOptions graph_options = 10;

public ConfigProto.Builder setInterOpParallelismThreads (int value)

 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.
 Negative means all operations are performed in caller's thread.
 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 ConfigProto.Builder setIntraOpParallelismThreads (int value)

 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.
 If you create an ordinary session, e.g., from Python or C++,
 then there is exactly one intra op thread pool per process.
 The first session created determines the number of threads in this pool.
 All subsequent sessions reuse/share this one global pool.
 There are notable exceptions to the default behavior describe above:
 1. There is an environment variable  for overriding this thread pool,
    named TF_OVERRIDE_GLOBAL_THREADPOOL.
 2. When connecting to a server, such as a remote `tf.train.Server`
    instance, then this option will be ignored altogether.
 
int32 intra_op_parallelism_threads = 2;

public ConfigProto.Builder setIsolateSessionState (boolean value)

 If true, any resources such as Variables used in the session will not be
 shared with other sessions. However, when clusterspec propagation is
 enabled, this field is ignored and sessions are always isolated.
 
bool isolate_session_state = 15;

public ConfigProto.Builder setLogDevicePlacement (boolean value)

 Whether device placements should be logged.
 
bool log_device_placement = 8;

public ConfigProto.Builder setOperationTimeoutInMs (long value)

 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 ConfigProto.Builder setPlacementPeriod (int value)

 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 ConfigProto.Builder setRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor field, int index, Object value)

public ConfigProto.Builder setRpcOptions (RPCOptions value)

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public ConfigProto.Builder setRpcOptions (RPCOptions.Builder builderForValue)

 Options that apply when this session uses the distributed runtime.
 
.tensorflow.RPCOptions rpc_options = 13;

public ConfigProto.Builder setSessionInterOpThreadPool (int index, ThreadPoolOptionProto value)

 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 ConfigProto.Builder setSessionInterOpThreadPool (int index, ThreadPoolOptionProto.Builder builderForValue)

 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 ConfigProto.Builder setShareClusterDevicesInSession (boolean value)

 When true, WorkerSessions are created with device attributes from the
 full cluster.
 This is helpful when a worker wants to partition a graph
 (for example during a PartitionedCallOp).
 
bool share_cluster_devices_in_session = 17;

public final ConfigProto.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)

public ConfigProto.Builder setUsePerSessionThreads (boolean value)

 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;