ConfigProtoOrBuilder

interfejs publiczny ConfigProtoOrBuilder

Znane podklasy pośrednie ConfigProto , ConfigProto.Builder KonfiguracjaProto Session configuration parameters.  ConfigProto.Builder Session configuration parameters.

Metody publiczne

abstrakcyjna wartość logiczna	zawieraDeviceCount (klucz ciąg) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use.
abstrakcyjna wartość logiczna	getAllowSoftPlacement () Whether soft placement is allowed.
abstrakcyjny ClusterDef	pobierzClusterDef () Optional list of all workers to use in this session.
abstrakcyjny ClusterDefOrBuilder	getClusterDefOrBuilder () Optional list of all workers to use in this session.
abstrakcyjna Mapa<String, Integer>	pobierz liczbę urządzeń () Zamiast tego użyj getDeviceCountMap() .
streszczenie wew	getDeviceCountCount () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use.
abstrakcyjna Mapa<String, Integer>	getDeviceCountMap () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use.
streszczenie wew	getDeviceCountOrDefault (klucz ciągu, int wartość domyślna) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use.
streszczenie wew	getDeviceCountOrThrow (klucz ciąg) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use.
abstrakcyjny ciąg	getDeviceFilters (indeks int) When any filters are present sessions will ignore all devices which do not match the filters.
streszczenie com.google.protobuf.ByteString	getDeviceFiltersBytes (indeks int) When any filters are present sessions will ignore all devices which do not match the filters.
streszczenie wew	pobierz liczbę filtrów urządzeń () When any filters are present sessions will ignore all devices which do not match the filters.
lista abstrakcyjna<String>	pobierz listę filtrów urządzeń () When any filters are present sessions will ignore all devices which do not match the filters.
streszczenie ConfigProto.Experimental	uzyskaj eksperymentalny () .tensorflow.ConfigProto.Experimental experimental = 16;
streszczenie ConfigProto.ExperimentalOrBuilder	getExperimentalOrBuilder () .tensorflow.ConfigProto.Experimental experimental = 16;
abstrakcyjne opcje GPU	getGpuOptions () Options that apply to all GPUs.
abstrakcyjne opcje GPUOrBuilder	getGpuOptionsOrBuilder () Options that apply to all GPUs.
abstrakcyjne opcje wykresu	getGraphOptions () Options that apply to all graphs.
streszczenie GraphOptionsOrBuilder	getGraphOptionsOrBuilder () Options that apply to all graphs.
streszczenie wew	getInterOpParallelismThreads () Nodes that perform blocking operations are enqueued on a pool of inter_op_parallelism_threads available in each process.
streszczenie wew	getIntraOpParallelismThreads () The execution of an individual op (for some op types) can be parallelized on a pool of intra_op_parallelism_threads.
abstrakcyjna wartość logiczna	getIsolateSessionState () If true, any resources such as Variables used in the session will not be shared with other sessions.
abstrakcyjna wartość logiczna	getLogDevicePlacement () Whether device placements should be logged.
abstrakcyjne, długie	getOperationTimeoutInMs () Global timeout for all blocking operations in this session.
streszczenie wew	pobierzPlacementPeriod () 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).
abstrakcyjne opcje RPCOptions	getRpcOptions () Options that apply when this session uses the distributed runtime.
streszczenie RPCOptionsOrBuilder	getRpcOptionsOrBuilder () Options that apply when this session uses the distributed runtime.
streszczenie ThreadPoolOptionProto	getSessionInterOpThreadPool (indeks int) This option is experimental - it may be replaced with a different mechanism in the future.
streszczenie wew	getSessionInterOpThreadPoolCount () This option is experimental - it may be replaced with a different mechanism in the future.
lista abstrakcyjna< ThreadPoolOptionProto >	getSessionInterOpThreadPoolList () This option is experimental - it may be replaced with a different mechanism in the future.
streszczenie ThreadPoolOptionProtoOrBuilder	getSessionInterOpThreadPoolOrBuilder (indeks int) This option is experimental - it may be replaced with a different mechanism in the future.
lista abstrakcyjna<? rozszerza ThreadPoolOptionProtoOrBuilder >	getSessionInterOpThreadPoolOrBuilderList () This option is experimental - it may be replaced with a different mechanism in the future.
abstrakcyjna wartość logiczna	getShareClusterDevicesInSession () When true, WorkerSessions are created with device attributes from the full cluster.
abstrakcyjna wartość logiczna	getUsePerSessionThreads () If true, use a new set of threads for this session rather than the global pool of threads.
abstrakcyjna wartość logiczna	maClusterDef () Optional list of all workers to use in this session.
abstrakcyjna wartość logiczna	maEksperymentalny () .tensorflow.ConfigProto.Experimental experimental = 16;
abstrakcyjna wartość logiczna	maGpuOpcje () Options that apply to all GPUs.
abstrakcyjna wartość logiczna	maGraphOptions () Options that apply to all graphs.
abstrakcyjna wartość logiczna	maRpcOptions () Options that apply when this session uses the distributed runtime.

Metody publiczne

publiczna wartość logiczna abstrakcyjna zawieraDeviceCount (klucz ciąg)

 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;

publiczna abstrakcja logiczna 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;

publiczne streszczenie ClusterDef getClusterDef ()

 Optional list of all workers to use in this session.
 

.tensorflow.ClusterDef cluster_def = 14;

publiczne streszczenie ClusterDefOrBuilder getClusterDefOrBuilder ()

 Optional list of all workers to use in this session.
 

.tensorflow.ClusterDef cluster_def = 14;

publiczna abstrakcja Mapa<String, Integer> getDeviceCount ()

Zamiast tego użyj getDeviceCountMap() .

publiczne streszczenie 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;

publiczna abstrakcja Mapa<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 streszczenie int getDeviceCountOrDefault (klucz string, int wartość domyślna)

 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 streszczenie int getDeviceCountOrThrow (klucz typu 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;

public streszczenie Ciąg 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;

publiczne streszczenie 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;

publiczne streszczenie 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;

publiczna lista abstrakcyjna<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;

publiczne streszczenie ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

publiczne streszczenie ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

publiczne streszczenie GPUOptions getGpuOptions ()

 Options that apply to all GPUs.
 

.tensorflow.GPUOptions gpu_options = 6;

publiczne streszczenie GPUOptionsOrBuilder getGpuOptionsOrBuilder ()

 Options that apply to all GPUs.
 

.tensorflow.GPUOptions gpu_options = 6;

publiczne streszczenie GraphOptions getGraphOptions ()

 Options that apply to all graphs.
 

.tensorflow.GraphOptions graph_options = 10;

publiczne streszczenie GraphOptionsOrBuilder getGraphOptionsOrBuilder ()

 Options that apply to all graphs.
 

.tensorflow.GraphOptions graph_options = 10;

publiczne streszczenie 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;

publiczne streszczenie 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;

publiczna wartość logiczna abstrakcyjna 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;

publiczna abstrakcja logiczna getLogDevicePlacement ()

 Whether device placements should be logged.
 

bool log_device_placement = 8;

publiczne streszczenie długie 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;

publiczne streszczenie 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;

publiczne streszczenie RPCOptions getRpcOptions ()

 Options that apply when this session uses the distributed runtime.
 

.tensorflow.RPCOptions rpc_options = 13;

publiczne streszczenie RPCOptionsOrBuilder getRpcOptionsOrBuilder ()

 Options that apply when this session uses the distributed runtime.
 

.tensorflow.RPCOptions rpc_options = 13;

publiczna abstrakcja ThreadPoolOptionProto getSessionInterOpThreadPool (indeks 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;

publiczne streszczenie 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;

publiczna lista abstrakcyjna< 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;

publiczna abstrakcja ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (indeks 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;

publiczna lista abstrakcyjna<? rozszerza 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;

publiczna wartość logiczna abstrakcyjna 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;

publiczna abstrakcja logiczna 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;

publiczna wartość logiczna abstrakcyjna hasClusterDef ()

 Optional list of all workers to use in this session.
 

.tensorflow.ClusterDef cluster_def = 14;

publiczna wartość logiczna abstrakcyjna maExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

publiczna wartość logiczna abstrakcyjna hasGpuOptions ()

 Options that apply to all GPUs.
 

.tensorflow.GPUOptions gpu_options = 6;

publiczna wartość logiczna abstrakcyjna hasGraphOptions ()

 Options that apply to all graphs.
 

.tensorflow.GraphOptions graph_options = 10;

publiczna wartość logiczna abstrakcyjna hasRpcOptions ()

 Options that apply when this session uses the distributed runtime.
 

.tensorflow.RPCOptions rpc_options = 13;