ConfigProto.Builder

kelas akhir statis publik ConfigProto.Builder

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

Metode Publik

ConfigProto.Builder
addAllDeviceFilters (nilai<String> yang dapat diubah)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addAllSessionInterOpThreadPool (Nilai Iterable<? extends ThreadPoolOptionProto >)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addDeviceFilters (Nilai string)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addDeviceFiltersBytes (nilai com.google.protobuf.ByteString)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
addRepeatedField (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)
ConfigProto.Builder
addSessionInterOpThreadPool (nilai ThreadPoolOptionProto )
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool (indeks int, nilai ThreadPoolOptionProto )
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool ( ThreadPoolOptionProto.Pembuat pembangunForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
addSessionInterOpThreadPool (indeks int, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
addSessionInterOpThreadPoolBuilder (indeks int)
 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.
KonfigurasiProto
KonfigurasiProto
ConfigProto.Builder
jernih ()
ConfigProto.Builder
clearAllowSoftPlacement ()
 Whether soft placement is allowed.
ConfigProto.Builder
hapusClusterDef ()
 Optional list of all workers to use in this session.
ConfigProto.Builder
ConfigProto.Builder
hapusFilter Perangkat ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
jelasEksperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
clearField (bidang com.google.protobuf.Descriptors.FieldDescriptor)
ConfigProto.Builder
hapusGpuOptions ()
 Options that apply to all GPUs.
ConfigProto.Builder
hapusGraphOptions ()
 Options that apply to all graphs.
ConfigProto.Builder
hapusInterOpParallelismThreads ()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ConfigProto.Builder
jelasIntraOpParallelismThreads ()
 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
hapusOperationTimeoutInMs ()
 Global timeout for all blocking operations in this session.
ConfigProto.Builder
periode penempatan yang jelas ()
 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
hapusRpcOptions ()
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
hapusSesiInterOpThreadPool ()
 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
hapusUsePerSessionThreads ()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
ConfigProto.Builder
klon ()
boolean
berisiDeviceCount (kunci string)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
boolean
dapatkanAllowSoftPlacement ()
 Whether soft placement is allowed.
ClusterDef
dapatkanClusterDef ()
 Optional list of all workers to use in this session.
ClusterDef.Pembangun
dapatkanClusterDefBuilder ()
 Optional list of all workers to use in this session.
ClusterDefOrBuilder
dapatkanClusterDefOrBuilder ()
 Optional list of all workers to use in this session.
KonfigurasiProto
com.google.protobuf.Descriptors.Descriptor statis terakhir
com.google.protobuf.Descriptors.Descriptor
Peta<String, Integer>
dapatkan Jumlah Perangkat ()
Gunakan getDeviceCountMap() sebagai gantinya.
ke dalam
dapatkanDeviceCountCount ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Peta<String, Integer>
dapatkanDeviceCountMap ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ke dalam
getDeviceCountOrDefault (kunci string, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ke dalam
getDeviceCountOrThrow (kunci string)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Rangkaian
getDeviceFilters (indeks int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ByteString
getDeviceFiltersBytes (indeks int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ke dalam
dapatkanDeviceFiltersCount ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ProtocolStringList
dapatkanDaftarFilterPerangkat ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Eksperimental
dapatkan Eksperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Eksperimental.Builder
dapatkanExperimentalBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder
dapatkanExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
Opsi GPU
dapatkanGpuOptions ()
 Options that apply to all GPUs.
Opsi GPU.Pembangun
dapatkanGpuOptionsBuilder ()
 Options that apply to all GPUs.
GPUOptionsOrBuilder
dapatkanGpuOptionsOrBuilder ()
 Options that apply to all GPUs.
Opsi Grafik
dapatkanGraphOptions ()
 Options that apply to all graphs.
GraphOptions.Builder
dapatkanGraphOptionsBuilder ()
 Options that apply to all graphs.
GraphOptionsOrBuilder
dapatkanGraphOptionsOrBuilder ()
 Options that apply to all graphs.
ke dalam
dapatkanInterOpParallelismThreads ()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ke dalam
dapatkanIntraOpParallelismThreads ()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
boolean
dapatkanIsolateSessionState ()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
boolean
dapatkanLogDevicePlacement ()
 Whether device placements should be logged.
Peta<String, Integer>
dapatkanMutableDeviceCount ()
Gunakan pengakses mutasi alternatif sebagai gantinya.
panjang
dapatkanOperationTimeoutInMs ()
 Global timeout for all blocking operations in this session.
ke dalam
dapatkanPeriode Penempatan ()
 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).
Opsi RPCO
dapatkanRpcOptions ()
 Options that apply when this session uses the distributed runtime.
RPCOptions.Pembangun
dapatkanRpcOptionsBuilder ()
 Options that apply when this session uses the distributed runtime.
RPCOptionsOrBuilder
dapatkanRpcOptionsOrBuilder ()
 Options that apply when this session uses the distributed runtime.
ThreadPoolOptionProto
getSessionInterOpThreadPool (indeks int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProto.Builder
getSessionInterOpThreadPoolBuilder (indeks int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Daftar< ThreadPoolOptionProto.Builder >
getSessionInterOpThreadPoolBuilderList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ke dalam
getSessionInterOpThreadPoolCount ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Daftar< ThreadPoolOptionProto >
dapatkanSessionInterOpThreadPoolList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder (indeks int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Daftar<? memperluas ThreadPoolOptionProtoOrBuilder >
getSessionInterOpThreadPoolOrBuilderList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
boolean
dapatkanShareClusterDevicesInSession ()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
boolean
dapatkanUsePerSessionThreads ()
 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
memiliki Eksperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
boolean
memilikiGpuOptions ()
 Options that apply to all GPUs.
boolean
hasGraphOptions ()
 Options that apply to all graphs.
boolean
memilikiRpcOptions ()
 Options that apply when this session uses the distributed runtime.
boolean terakhir
ConfigProto.Builder
mergeClusterDef (nilai ClusterDef )
 Optional list of all workers to use in this session.
ConfigProto.Builder
mergeExperimental ( ConfigProto.Nilai eksperimental)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
mergeFrom (com.google.protobuf.Pesan lainnya)
ConfigProto.Builder
mergeFrom (com.google.protobuf.CodedInputStream masukan, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
ConfigProto.Builder
mergeGpuOptions (nilai GPUOptions )
 Options that apply to all GPUs.
ConfigProto.Builder
mergeGraphOptions (nilai GraphOptions )
 Options that apply to all graphs.
ConfigProto.Builder
mergeRpcOptions (nilai RPCOptions )
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder terakhir
mergeUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)
ConfigProto.Builder
putAllDeviceCount (nilai Peta<String, Integer>)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
putDeviceCount (kunci string, nilai int)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
hapusDeviceCount (kunci string)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
ConfigProto.Builder
hapusSessionInterOpThreadPool (int indeks)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setAllowSoftPlacement (nilai boolean)
 Whether soft placement is allowed.
ConfigProto.Builder
setClusterDef ( ClusterDef.Pembuat pembangunForValue)
 Optional list of all workers to use in this session.
ConfigProto.Builder
setClusterDef (nilai ClusterDef )
 Optional list of all workers to use in this session.
ConfigProto.Builder
setDeviceFilters (indeks int, nilai String)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Builder
setEksperimental ( ConfigProto.Nilai eksperimental)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
setEksperimental ( ConfigProto.Experimental.Pembuat pembangunForValue)
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.Builder
setField (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)
ConfigProto.Builder
setGpuOptions ( GPUOptions.Pembuat pembangunForValue)
 Options that apply to all GPUs.
ConfigProto.Builder
setGpuOptions (nilai GPUOptions )
 Options that apply to all GPUs.
ConfigProto.Builder
setGraphOptions ( GraphOptions.Pembuat pembangunForValue)
 Options that apply to all graphs.
ConfigProto.Builder
setGraphOptions (nilai GraphOptions )
 Options that apply to all graphs.
ConfigProto.Builder
setInterOpParallelismThreads (nilai int)
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
ConfigProto.Builder
setIntraOpParallelismThreads (nilai int)
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
ConfigProto.Builder
setIsolateSessionState (nilai boolean)
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
ConfigProto.Builder
setLogDevicePlacement (nilai boolean)
 Whether device placements should be logged.
ConfigProto.Builder
setOperationTimeoutInMs (nilai panjang)
 Global timeout for all blocking operations in this session.
ConfigProto.Builder
setPlacementPeriod (nilai int)
 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, indeks int, Nilai objek)
ConfigProto.Builder
setRpcOptions (nilai RPCOptions )
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
setRpcOptions ( RPCOptions.Pembuat pembangunForValue)
 Options that apply when this session uses the distributed runtime.
ConfigProto.Builder
setSessionInterOpThreadPool (indeks int, nilai ThreadPoolOptionProto )
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setSessionInterOpThreadPool (indeks int, ThreadPoolOptionProto.Builder builderForValue)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ConfigProto.Builder
setShareClusterDevicesInSession (nilai boolean)
 When true, WorkerSessions are created with device attributes from the
 full cluster.
ConfigProto.Builder terakhir
setUnknownFields (com.google.protobuf.UnknownFieldSet unknownFields)
ConfigProto.Builder
setUsePerSessionThreads (nilai boolean)
 If true, use a new set of threads for this session rather than the global
 pool of threads.

Metode Warisan

Metode Publik

public ConfigProto.Builder addAllDeviceFilters (nilai<String> yang dapat diubah)

 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 (Nilai Iterable<? extends ThreadPoolOptionProto >)

 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 (Nilai string)

 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;

ConfigProto.Builder publik addDeviceFiltersBytes (nilai com.google.protobuf.ByteString)

 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)

ConfigProto.Builder publik addSessionInterOpThreadPool (nilai ThreadPoolOptionProto )

 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;

ConfigProto.Builder publik addSessionInterOpThreadPool (indeks int, nilai ThreadPoolOptionProto )

 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;

ConfigProto.Builder addSessionInterOpThreadPool publik ( 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;

ConfigProto.Builder publik addSessionInterOpThreadPool (int indeks, 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;

ThreadPoolOptionProto.Builder publik addSessionInterOpThreadPoolBuilder (int indeks)

 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;

ThreadPoolOptionProto.Builder publik 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;

build ConfigProto publik ()

build ConfigProto publikPartial ()

ConfigProto.Builder publik jelas ()

ConfigProto.Builder publik 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;

ConfigProto.Builder publik clearClusterDef ()

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

ConfigProto.Builder publik clearDeviceCount ()

ConfigProto.Builder publik 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;

ConfigProto.Builder publik clearExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

ConfigProto.Builder clearField publik (bidang com.google.protobuf.Descriptors.FieldDescriptor)

ConfigProto.Builder publik clearGpuOptions ()

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

ConfigProto.Builder publik clearGraphOptions ()

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

ConfigProto.Builder publik 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;

ConfigProto.Builder publik 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;

ConfigProto.Builder publik 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;

ConfigProto.Builder publik clearLogDevicePlacement ()

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

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

ConfigProto.Builder publik 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;

ConfigProto.Builder publik 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;

ConfigProto.Builder publik clearRpcOptions ()

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

ConfigProto.Builder publik 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;

ConfigProto.Builder publik 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;

ConfigProto.Builder publik 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;

klon ConfigProto.Builder publik ()

boolean publik berisiDeviceCount (kunci string)

 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;

getAllowSoftPlacement boolean publik ()

 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;

ClusterDef publik getClusterDef ()

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

ClusterDef.Builder publik getClusterDefBuilder ()

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

ClusterDefOrBuilder publik getClusterDefOrBuilder ()

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

ConfigProto publik getDefaultInstanceForType ()

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

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

Peta publik<String, Integer> getDeviceCount ()

Gunakan getDeviceCountMap() sebagai gantinya.

int publik 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;

Peta publik<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 (kunci string, 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 (kunci string)

 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;

String publik getDeviceFilters (indeks int)

 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;

publik com.google.protobuf.ByteString getDeviceFiltersBytes (indeks int)

 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;

int publik 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;

publik 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;

ConfigProto publik.Eksperimental getEksperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

ConfigProto.Experimental.Builder publik getExperimentalBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

ConfigProto.ExperimentalOrBuilder publik getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

Opsi GPU publik getGpuOptions ()

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

GPUOptions.Builder publik getGpuOptionsBuilder ()

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

GPUOptionsOrBuilder publik getGpuOptionsOrBuilder ()

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

GraphOptions publik getGraphOptions ()

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

GraphOptions.Builder publik getGraphOptionsBuilder ()

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

publik GraphOptionsOrBuilder getGraphOptionsOrBuilder ()

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

publik 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;

publik 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;

boolean publik 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;

getLogDevicePlacement boolean publik ()

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

Peta publik<String, Integer> getMutableDeviceCount ()

Gunakan pengakses mutasi alternatif sebagai gantinya.

getOperationTimeoutInMs publik yang panjang ()

 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;

int publik 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;

RPCOptions publik getRpcOptions ()

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

RPCOptions.Builder publik getRpcOptionsBuilder ()

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

RPCOptionsOrBuilder publik getRpcOptionsOrBuilder ()

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

ThreadPoolOptionProto publik getSessionInterOpThreadPool (int indeks)

 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;

ThreadPoolOptionProto.Builder publik getSessionInterOpThreadPoolBuilder (int indeks)

 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;

Daftar publik< 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;

int publik 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;

Daftar publik< 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;

publik ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (int indeks)

 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;

Daftar Publik<? memperluas 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;

boolean publik 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;

boolean publik 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;

boolean publik hasClusterDef ()

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

boolean publik hasEksperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

boolean publik hasGpuOptions ()

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

boolean publik hasGraphOptions ()

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

boolean publik hasRpcOptions ()

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

boolean akhir publik diinisialisasi ()

ConfigProto.Builder mergeClusterDef publik (nilai ClusterDef )

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

ConfigProto.Builder mergeExperimental publik ( ConfigProto.Nilai eksperimental )

.tensorflow.ConfigProto.Experimental experimental = 16;

ConfigProto.Builder mergeFrom publik (com.google.protobuf.Message lainnya)

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

Melempar
Pengecualian IO

ConfigProto.Builder mergeGpuOptions publik (nilai GPUOptions )

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

ConfigProto.Builder mergeGraphOptions publik (nilai GraphOptions )

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

ConfigProto.Builder mergeRpcOptions publik (nilai RPCOptions )

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

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

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

 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 (kunci string, nilai int)

 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;

ConfigProto.Builder publik deleteDeviceCount (kunci string)

 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;

ConfigProto.Builder publik hapusSessionInterOpThreadPool (int indeks)

 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;

ConfigProto.Builder setAllowSoftPlacement publik (nilai boolean)

 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;

ConfigProto.Builder setClusterDef publik ( ClusterDef.Builder builderForValue)

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

ConfigProto.Builder setClusterDef publik (nilai ClusterDef )

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

ConfigProto.Builder setDeviceFilters publik (indeks int, nilai String)

 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;

ConfigProto.Builder setExperimental publik ( ConfigProto.Nilai eksperimental )

.tensorflow.ConfigProto.Experimental experimental = 16;

ConfigProto.Builder setEksperimental publik ( ConfigProto.Experimental.Builder builderForValue)

.tensorflow.ConfigProto.Experimental experimental = 16;

public ConfigProto.Builder setField (bidang com.google.protobuf.Descriptors.FieldDescriptor, Nilai objek)

ConfigProto.Builder setGpuOptions publik ( GPUOptions.Builder builderForValue)

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

ConfigProto.Builder setGpuOptions publik (nilai GPUOptions )

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

ConfigProto.Builder setGraphOptions publik ( GraphOptions.Builder builderForValue)

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

ConfigProto.Builder setGraphOptions publik (nilai GraphOptions )

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

ConfigProto.Builder setInterOpParallelismThreads publik (nilai int)

 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 (nilai int)

 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 (nilai boolean)

 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;

ConfigProto.Builder setLogDevicePlacement publik (nilai boolean)

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

public ConfigProto.Builder setOperationTimeoutInMs (nilai panjang)

 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;

ConfigProto.Builder setPlacementPeriod publik (nilai int)

 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 (bidang com.google.protobuf.Descriptors.FieldDescriptor, indeks int, Nilai objek)

ConfigProto.Builder setRpcOptions publik (nilai RPCOptions )

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

ConfigProto.Builder setRpcOptions publik ( RPCOptions.Builder builderForValue)

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

ConfigProto.Builder publik setSessionInterOpThreadPool (indeks int, nilai ThreadPoolOptionProto )

 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;

ConfigProto.Builder setSessionInterOpThreadPool publik (indeks int, 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;

ConfigProto.Builder publik setShareClusterDevicesInSession (nilai boolean)

 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;

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

public ConfigProto.Builder setUsePerSessionThreads (nilai boolean)

 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;