ConfigProtoOrBuilder

общедоступный интерфейс ConfigProtoOrBuilder
Известные косвенные подклассы

Публичные методы

абстрактное логическое значение
содержитDeviceCount (строковый ключ)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
абстрактное логическое значение
getAllowSoftPlacement ()
 Whether soft placement is allowed.
абстрактный кластердеф
getClusterDef ()
 Optional list of all workers to use in this session.
абстрактный ClusterDefOrBuilder
getClusterDefOrBuilder ()
 Optional list of all workers to use in this session.
абстрактная карта<String, Integer>
getDeviceCount ()
Вместо этого используйте getDeviceCountMap() .
абстрактный интервал
getDeviceCountCount ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
абстрактная карта<String, Integer>
getDeviceCountMap ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
абстрактный интервал
getDeviceCountOrDefault (строковый ключ, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
абстрактный интервал
getDeviceCountOrThrow (строковый ключ)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
абстрактная строка
getDeviceFilters (индекс целого числа)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
абстрактный com.google.protobuf.ByteString
getDeviceFiltersBytes (индекс целого числа)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
абстрактный интервал
getDeviceFiltersCount ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
абстрактный список<String>
getDeviceFiltersList ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
абстрактный ConfigProto.Экспериментальный
получитьЭкспериментальный ()
.tensorflow.ConfigProto.Experimental experimental = 16;
абстрактный ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
абстрактные параметры графического процессора
получитьGpuOptions ()
 Options that apply to all GPUs.
абстрактный GPUOptionsOrBuilder
getGpuOptionsOrBuilder ()
 Options that apply to all GPUs.
абстрактные графические параметры
getGraphOptions ()
 Options that apply to all graphs.
абстрактный GraphOptionsOrBuilder
getGraphOptionsOrBuilder ()
 Options that apply to all graphs.
абстрактный интервал
getInterOpParallelismThreads ()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
абстрактный интервал
getIntraOpParallelismThreads ()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
абстрактное логическое значение
getIsolateSessionState ()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
абстрактное логическое значение
getLogDevicePlacement ()
 Whether device placements should be logged.
абстрактный длинный
getOperationTimeoutInMs ()
 Global timeout for all blocking operations in this session.
абстрактный интервал
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).
абстрактные параметры RPCOptions
getRpcOptions ()
 Options that apply when this session uses the distributed runtime.
абстрактный RPCOptionsOrBuilder
getRpcOptionsOrBuilder ()
 Options that apply when this session uses the distributed runtime.
абстрактный ThreadPoolOptionProto
getSessionInterOpThreadPool (индекс int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
абстрактный интервал
getSessionInterOpThreadPoolCount ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
абстрактный список < ThreadPoolOptionProto >
getSessionInterOpThreadPoolList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
абстрактный ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder (индекс int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
абстрактный список<? расширяет ThreadPoolOptionProtoOrBuilder >
getSessionInterOpThreadPoolOrBuilderList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
абстрактное логическое значение
getShareClusterDevicesInSession ()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
абстрактное логическое значение
getUsePerSessionThreads ()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
абстрактное логическое значение
имеетКластерДеф ()
 Optional list of all workers to use in this session.
абстрактное логическое значение
имеетЭкспериментальный ()
.tensorflow.ConfigProto.Experimental experimental = 16;
абстрактное логическое значение
имеетGpuOptions ()
 Options that apply to all GPUs.
абстрактное логическое значение
имеетграфопционы ()
 Options that apply to all graphs.
абстрактное логическое значение
имеетRpcOptions ()
 Options that apply when this session uses the distributed runtime.

Публичные методы

общедоступное абстрактное логическое значение containsDeviceCount (строковый ключ)

 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;

общедоступное абстрактное логическое значение 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;

публичный абстрактный ClusterDef getClusterDef ()

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

публичный абстрактный ClusterDefOrBuilder getClusterDefOrBuilder ()

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

общедоступная абстрактная карта Map<String, Integer> getDeviceCount ()

Вместо этого используйте getDeviceCountMap() .

общедоступный абстрактный 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;

публичная абстрактная карта Map<String, Integer> getDeviceCountMap ()

 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.  If a particular device
 type is not found in the map, the system picks an appropriate
 number.
 
map<string, int32> device_count = 1;

общедоступный абстрактный int getDeviceCountOrDefault (строковый ключ, 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;

общедоступный абстрактный int getDeviceCountOrThrow (строковый ключ)

 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;

общедоступная абстрактная строка getDeviceFilters (индекс 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;

общедоступный абстрактный com.google.protobuf.ByteString getDeviceFiltersBytes (индекс 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;

общедоступный абстрактный 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;

общедоступный абстрактный список <String> 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;

публичный абстрактный ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

публичный абстрактный ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

публичный абстрактный GPUOptions getGpuOptions ()

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

общедоступный абстрактный GPUOptionsOrBuilder getGpuOptionsOrBuilder ()

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

публичный абстрактный GraphOptions getGraphOptions ()

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

публичный абстрактный GraphOptionsOrBuilder getGraphOptionsOrBuilder ()

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

public Abstract 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 Abstract 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;

общедоступное абстрактное логическое значение 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;

общедоступное абстрактное логическое значение getLogDevicePlacement ()

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

общедоступный абстрактный длинный 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;

публичный абстрактный 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;

публичный абстрактный RPCOptions getRpcOptions ()

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

общедоступный абстрактный RPCOptionsOrBuilder getRpcOptionsOrBuilder ()

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

общедоступный абстрактный ThreadPoolOptionProto getSessionInterOpThreadPool (индекс int)

 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;

общедоступный абстрактный 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;

общедоступный абстрактный список < 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;

общедоступный абстрактный ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (индекс int)

 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;

публичный абстрактный список<? расширяет 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;

общедоступное абстрактное логическое значение 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;

общедоступное абстрактное логическое значение 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;

общедоступное абстрактное логическое значение hasClusterDef ()

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

общедоступное абстрактное логическое значение hasExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

общедоступное абстрактное логическое значение hasGpuOptions ()

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

общедоступное абстрактное логическое значение hasGraphOptions ()

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

общедоступное абстрактное логическое значение hasRpcOptions ()

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