ConfigProto.Builder

genel statik son sınıf ConfigProto.Builder

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

Genel Yöntemler

ConfigProto.Builder
addAllDeviceFilters (Yinelenebilir<String> değerleri)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addAllSessionInterOpThreadPool (Yinelenebilir<? ThreadPoolOptionProto > değerlerini genişletir)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addDeviceFilters (Dize değeri)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addDeviceFiltersBytes (com.google.protobuf.ByteString değeri)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addRepeatedField (com.google.protobuf.Descriptors.FieldDescriptor alanı, Nesne değeri)
ConfigProto.Builder
addSessionInterOpThreadPool ( ThreadPoolOptionProto değeri)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool (int dizini, ThreadPoolOptionProto değeri)
 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 dizini, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
addSessionInterOpThreadPoolBuilder (int dizini)
 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.
Yapılandırma Protokolü
Yapılandırma Protokolü
ConfigProto.Builder
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
clearDeneysel ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
clearField (com.google.protobuf.Descriptors.FieldDescriptor alanı)
ConfigProto.Builder
clearGpuSeçenekleri ()
 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
clearIsulateSessionState ()
 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
klon ()
boolean
içerirDeviceCount (Dize anahtarı)
 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.
KümeDef
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.
Yapılandırma Protokolü
final statik com.google.protobuf.Descriptors.Descriptor
com.google.protobuf.Descriptors.Descriptor
Harita<Dize, Tamsayı>
getDeviceCount ()
Bunun yerine getDeviceCountMap() kullanın.
int
getDeviceCountCount ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Harita<Dize, Tamsayı>
getDeviceCountMap ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrDefault (Dize anahtarı, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
int
getDeviceCountOrThrow (Dize tuşu)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Sicim
getDeviceFilters (int dizini)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ByteString
getDeviceFiltersBytes (int dizini)
 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.Deneysel
getDeneysel ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Experimental.Builder
getExperimentalBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
GPUSeçenekleri
getGpuOptions ()
 Options that apply to all GPUs.
GPUOptions.Builder
getGpuOptionsBuilder ()
 Options that apply to all GPUs.
GPUOptionsOrBuilder
getGpuOptionsOrBuilder ()
 Options that apply to all GPUs.
GrafikSeçenekleri
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
getIsulateSessionState ()
 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.
Harita<Dize, Tamsayı>
getMutableDeviceCount ()
Bunun yerine alternatif mutasyon erişimcilerini kullanın.
uzun
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).
RPCSeçenekleri
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 dizini)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
getSessionInterOpThreadPoolBuilder (int dizini)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Liste< 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.
Listele< ThreadPoolOptionProto >
getSessionInterOpThreadPoolList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder (int dizini)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Liste<? ThreadPoolOptionProtoOrBuilder'ı genişletir >
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
vardırDeneysel ()
.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.
son boole değeri
ConfigProto.Builder
mergeClusterDef ( ClusterDef değeri)
 Optional list of all workers to use in this session.
ConfigProto.Builder
mergeExperimental ( ConfigProto.Experimental değer)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
mergeFrom (com.google.protobuf.Message other)
ConfigProto.Builder
mergeFrom (com.google.protobuf.CodedInputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
ConfigProto.Builder
mergeGpuOptions ( GPUOptions değeri)
 Options that apply to all GPUs.
ConfigProto.Builder
mergeGraphOptions ( GraphOptions değeri)
 Options that apply to all graphs.
ConfigProto.Builder
mergeRpcOptions ( RPCOptions değeri)
 Options that apply when this session uses the distributed runtime.
son ConfigProto.Builder
mergeUnknownFields (com.google.protobuf.UnknownFieldSet bilinmiyorFields)
ConfigProto.Builder
putAllDeviceCount (Map<String, Integer> değerleri)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
putDeviceCount (Dize anahtarı, int değeri)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
RemoveDeviceCount (Dize tuşu)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
RemoveSessionInterOpThreadPool (int dizini)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setAllowSoftPlacement (boole değeri)
 Whether soft placement is allowed.
ConfigProto.Builder
setClusterDef ( ClusterDef.Builder oluşturucuForValue)
 Optional list of all workers to use in this session.
ConfigProto.Builder
setClusterDef ( ClusterDef değeri)
 Optional list of all workers to use in this session.
ConfigProto.Builder
setDeviceFilters (int dizini, Dize değeri)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
setExperimental ( ConfigProto.Experimental değer)
.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 alanı, Nesne değeri)
ConfigProto.Builder
setGpuOptions ( GPUOptions.Builder builderForValue)
 Options that apply to all GPUs.
ConfigProto.Builder
setGpuOptions ( GPUOptions değeri)
 Options that apply to all GPUs.
ConfigProto.Builder
setGraphOptions ( GraphOptions.Builder builderForValue)
 Options that apply to all graphs.
ConfigProto.Builder
setGraphOptions ( GraphOptions değeri)
 Options that apply to all graphs.
ConfigProto.Builder
setInterOpParallelismThreads (int değeri)
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ConfigProto.Builder
setIntraOpParallelismThreads (int değeri)
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
ConfigProto.Builder
setIsulateSessionState (boolean değeri)
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
ConfigProto.Builder
setLogDevicePlacement (boole değeri)
 Whether device placements should be logged.
ConfigProto.Builder
setOperationTimeoutInMs (uzun değer)
 Global timeout for all blocking operations in this session.
ConfigProto.Builder
setPlacementPeriod (int değeri)
 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 alanı, int dizini, Nesne değeri)
ConfigProto.Builder
setRpcOptions ( RPCOptions değeri)
 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 dizini, ThreadPoolOptionProto değeri)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setSessionInterOpThreadPool (int dizini, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setShareClusterDevicesInSession (boole değeri)
 When true, WorkerSessions are created with device attributes from the
 full cluster.
son ConfigProto.Builder
setUnknownFields (com.google.protobuf.UnknownFieldSet bilinmeyenFields)
ConfigProto.Builder
setUsePerSessionThreads (boole değeri)
 If true, use a new set of threads for this session rather than the global
 pool of threads.

Kalıtsal Yöntemler

Genel Yöntemler

public ConfigProto.Builder addAllDeviceFilters (Yinelenebilir<String> değerleri)

 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 (Yinelenebilir<? ThreadPoolOptionProto > değerlerini genişletir)

 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 (Dize değeri)

 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 değeri)

 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 alanı, Nesne değeri)

public ConfigProto.Builder addSessionInterOpThreadPool ( ThreadPoolOptionProto değeri)

 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 dizini, ThreadPoolOptionProto değeri)

 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 dizini, 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;

genel ThreadPoolOptionProto.Builder addSessionInterOpThreadPoolBuilder (int dizini)

 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;

genel 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;

genel ConfigProto yapısı ()

public ConfigProto buildPartial ()

genel ConfigProto.Builder clear ()

genel 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;

genel ConfigProto.Builder clearClusterDef ()

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

genel ConfigProto.Builder clearDeviceCount ()

genel 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;

genel ConfigProto.Builder clearField (com.google.protobuf.Descriptors.FieldDescriptor alanı)

genel ConfigProto.Builder clearGpuOptions ()

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

genel 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;

genel ConfigProto.Builder clearIsulateSessionState ()

 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;

genel 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;

genel 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;

genel ConfigProto.Builder klonu ()

public boolean includeDeviceCount (Dize anahtarı)

 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;

genel 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;

genel ClusterDef getClusterDef ()

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

genel ClusterDef.Builder getClusterDefBuilder ()

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

herkese açık 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 ()

genel Harita<String, Integer> getDeviceCount ()

Bunun yerine getDeviceCountMap() kullanın.

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;

genel Harita<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 (Dize anahtarı, 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 (Dize anahtarı)

 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 dizini)

 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 dizini)

 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;

genel GPUOptions getGpuOptions ()

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

genel GPUOptions.Builder getGpuOptionsBuilder ()

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

genel GPUOptionsOrBuilder getGpuOptionsOrBuilder ()

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

genel GraphOptions getGraphOptions ()

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

genel GraphOptions.Builder getGraphOptionsBuilder ()

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

genel 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;

genel boolean getIsulateSessionState ()

 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;

genel boolean getLogDevicePlacement ()

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

genel Harita<String, Integer> getMutableDeviceCount ()

Bunun yerine alternatif mutasyon erişimcilerini kullanın.

genel uzun 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;

genel RPCOptions getRpcOptions ()

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

genel 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;

genel ThreadPoolOptionProto getSessionInterOpThreadPool (int dizini)

 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;

genel ThreadPoolOptionProto.Builder getSessionInterOpThreadPoolBuilder (int dizini)

 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;

genel Liste< 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;

genel Liste< 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;

genel ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (int dizini)

 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;

genel liste<? ThreadPoolOptionProtoOrBuilder'ı genişletir > 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;

genel 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;

genel 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;

genel 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;

genel boolean hasGpuOptions ()

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

genel boolean hasGraphOptions ()

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

genel boolean hasRpcOptions ()

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

genel final boolean isInitialized ()

public ConfigProto.Builder mergeClusterDef ( ClusterDef değeri)

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

public ConfigProto.Builder mergeExperimental ( ConfigProto.Experimental değer)

.tensorflow.ConfigProto.Experimental experimental = 16;

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

genel ConfigProto.Builder mergeFrom (com.google.protobuf.CodedInputStream girişi, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Atar
IOİstisnası

public ConfigProto.Builder mergeGpuOptions ( GPUOptions değeri)

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

public ConfigProto.Builder mergeGraphOptions ( GraphOptions değeri)

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

public ConfigProto.Builder mergeRpcOptions ( RPCOptions değeri)

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

genel final ConfigProto.Builder mergeUnknownFields (com.google.protobuf.UnknownFieldSet bilinmiyorFields)

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

 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 (Dize anahtarı, int değeri)

 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 kaldırDeviceCount (Dize anahtarı)

 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 kaldırSessionInterOpThreadPool (int dizini)

 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 (boole değeri)

 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 değeri)

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

public ConfigProto.Builder setDeviceFilters (int dizini, Dize değeri)

 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 değer)

.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 alanı, Nesne değeri)

public ConfigProto.Builder setGpuOptions ( GPUOptions.Builder builderForValue)

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

public ConfigProto.Builder setGpuOptions ( GPUOptions değeri)

 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 değeri)

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

public ConfigProto.Builder setInterOpParallelismThreads (int değeri)

 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 değeri)

 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 setIsulateSessionState (boolean değeri)

 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 (boole değeri)

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

public ConfigProto.Builder setOperationTimeoutInMs (uzun değer)

 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 değeri)

 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 alanı, int dizini, Nesne değeri)

public ConfigProto.Builder setRpcOptions ( RPCOptions değeri)

 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 dizini, ThreadPoolOptionProto değeri)

 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 dizini, 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 (boole değeri)

 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;

genel final ConfigProto.Builder setUnknownFields (com.google.protobuf.UnknownFieldSet bilinmiyorFields)

public ConfigProto.Builder setUsePerSessionThreads (boole değeri)

 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;