ConfigProto

clase final pública ConfigProto

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

Clases anidadas

clase ConfigProto.Builder
 Session configuration parameters. 
clase ConfigProto.Experimental
 Everything inside Experimental is subject to change and is not subject
 to API stability guarantees in
 https://www.tensorflow.org/guide/version_compat. 
interfaz ConfigProto.ExperimentalOrBuilder

Constantes

entero ALLOW_SOFT_PLACEMENT_FIELD_NUMBER
entero CLUSTER_DEF_FIELD_NUMBER
entero DEVICE_COUNT_FIELD_NUMBER
entero DEVICE_FILTERS_FIELD_NUMBER
entero EXPERIMENTAL_FIELD_NUMBER
entero GPU_OPTIONS_FIELD_NUMBER
entero GRAPH_OPTIONS_FIELD_NUMBER
entero INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER
entero INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER
entero ISOLATE_SESSION_STATE_FIELD_NUMBER
entero LOG_DEVICE_PLACEMENT_FIELD_NUMBER
entero OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER
entero LUGAR_PERIOD_CAMPO_NUMBER
entero RPC_OPTIONS_FIELD_NUMBER
entero SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER
entero SHARE_CLUSTER_DEVICES_IN_SESSION_FIELD_NUMBER
entero USE_PER_SESSION_THREADS_FIELD_NUMBER

Métodos públicos

booleano
contieneDeviceCount (clave de cadena)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
booleano
es igual (Objeto obj)
booleano
getAllowSoftPlacement ()
 Whether soft placement is allowed.
Definición de clúster
obtenerClusterDef ()
 Optional list of all workers to use in this session.
ClusterDefOrBuilder
getClusterDefOrBuilder ()
 Optional list of all workers to use in this session.
Proto de configuración estático
Proto de configuración
com.google.protobuf.Descriptors.Descriptor estático final
Mapa<Cadena, Entero>
getDeviceCount ()
Utilice getDeviceCountMap() en su lugar.
entero
getDeviceCountCount ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Mapa<Cadena, Entero>
getDeviceCountMap ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
entero
getDeviceCountOrDefault (clave de cadena, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
entero
getDeviceCountOrThrow (clave de cadena)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
Cadena
getDeviceFilters (índice int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ByteString
getDeviceFiltersBytes (índice int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
entero
getDeviceFiltersCount ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
com.google.protobuf.ProtocolStringList
getDeviceFiltersList ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
ConfigProto.Experimental
obtenerExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
Opciones de GPU
getGpuOptions ()
 Options that apply to all GPUs.
GPUOptionsOrBuilder
getGpuOptionsOrBuilder ()
 Options that apply to all GPUs.
Opciones de gráfico
getGraphOptions ()
 Options that apply to all graphs.
GraphOptionsOrBuilder
getGraphOptionsOrBuilder ()
 Options that apply to all graphs.
entero
getInterOpParallelismThreads ()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
entero
getIntraOpParallelismThreads ()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
booleano
getIsolateSessionState ()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
booleano
getLogDevicePlacement ()
 Whether device placements should be logged.
largo
getOperationTimeoutInMs ()
 Global timeout for all blocking operations in this session.
entero
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).
Opciones RPC
getRpcOptions ()
 Options that apply when this session uses the distributed runtime.
RPCOptionsOrBuilder
getRpcOptionsOrBuilder ()
 Options that apply when this session uses the distributed runtime.
entero
ThreadPoolOptionProto
getSessionInterOpThreadPool (índice int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
entero
getSessionInterOpThreadPoolCount ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Lista< ThreadPoolOptionProto >
getSessionInterOpThreadPoolList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder (índice int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Lista<? extiende ThreadPoolOptionProtoOrBuilder >
getSessionInterOpThreadPoolOrBuilderList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
booleano
getShareClusterDevicesInSession ()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
com.google.protobuf.UnknownFieldSet final
booleano
getUsePerSessionThreads ()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
booleano
tieneClusterDef ()
 Optional list of all workers to use in this session.
booleano
tieneExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
booleano
tieneGpuOptions ()
 Options that apply to all GPUs.
booleano
tiene opciones de gráfico ()
 Options that apply to all graphs.
booleano
tieneRpcOptions ()
 Options that apply when this session uses the distributed runtime.
entero
booleano final
ConfigProto.Builder estático
ConfigProto.Builder estático
newBuilder (prototipo ConfigProto )
ConfigProto.Builder
Proto de configuración estático
parseDelimitedFrom (entrada de InputStream)
Proto de configuración estático
parseDelimitedFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Proto de configuración estático
parseFrom (datos de ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Proto de configuración estático
parseFrom (entrada com.google.protobuf.CodedInputStream)
Proto de configuración estático
parseFrom (byte[] datos, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Proto de configuración estático
parseFrom (datos de ByteBuffer)
Proto de configuración estático
parseFrom (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensiónRegistry)
Proto de configuración estático
parseFrom (datos com.google.protobuf.ByteString)
Proto de configuración estático
parseFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)
Proto de configuración estático
parseFrom (com.google.protobuf.ByteString datos, com.google.protobuf.ExtensionRegistryLite extensiónRegistry)
estático
ConfigProto.Builder
vacío
writeTo (salida de com.google.protobuf.CodedOutputStream)

Métodos heredados

Constantes

int final estático público ALLOW_SOFT_PLACEMENT_FIELD_NUMBER

Valor constante: 7

int final estático público CLUSTER_DEF_FIELD_NUMBER

Valor constante: 14

int final estático público DEVICE_COUNT_FIELD_NUMBER

Valor constante: 1

int final estático público DEVICE_FILTERS_FIELD_NUMBER

Valor constante: 4

int final estático público EXPERIMENTAL_FIELD_NUMBER

Valor constante: 16

int final estático público GPU_OPTIONS_FIELD_NUMBER

Valor constante: 6

int final estático público GRAPH_OPTIONS_FIELD_NUMBER

Valor constante: 10

público estático final int INTER_OP_PARALLELISM_THREADS_FIELD_NUMBER

Valor constante: 5

int final estático público INTRA_OP_PARALLELISM_THREADS_FIELD_NUMBER

Valor constante: 2

int final estático público ISOLATE_SESSION_STATE_FIELD_NUMBER

Valor constante: 15

int final estático público LOG_DEVICE_PLACEMENT_FIELD_NUMBER

Valor constante: 8

int final estático público OPERATION_TIMEOUT_IN_MS_FIELD_NUMBER

Valor constante: 11

int final estático público PLACEMENT_PERIOD_FIELD_NUMBER

Valor constante: 3

int final estático público RPC_OPTIONS_FIELD_NUMBER

Valor constante: 13

int final estático público SESSION_INTER_OP_THREAD_POOL_FIELD_NUMBER

Valor constante: 12

público estático final int SHARE_CLUSTER_DEVICES_IN_SESSION_FIELD_NUMBER

Valor constante: 17

int final estático público USE_PER_SESSION_THREADS_FIELD_NUMBER

Valor constante: 9

Métodos públicos

booleano público contieneDeviceCount (clave de cadena)

 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;

público booleano es igual (Objeto obj)

getAllowSoftPlacement booleano público ()

 Whether soft placement is allowed. If allow_soft_placement is true,
 an op will be placed on CPU if
   1. there's no GPU implementation for the OP
 or
   2. no GPU devices are known or registered
 or
   3. need to co-locate with reftype input(s) which are from CPU.
 
bool allow_soft_placement = 7;

public ClusterDef getClusterDef ()

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

público ClusterDefOrBuilder getClusterDefOrBuilder ()

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

ConfigProto público estático getDefaultInstance ()

ConfigPro público para getDefaultInstanceForType ()

público estático final com.google.protobuf.Descriptors.Descriptor getDescriptor ()

Mapa público<Cadena, Entero> getDeviceCount ()

Utilice getDeviceCountMap() en su lugar.

público 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;

Mapa público<Cadena, Entero> 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 (clave de cadena, 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 (clave de cadena)

 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;

cadena pública getDeviceFilters (índice 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;

público com.google.protobuf.ByteString getDeviceFiltersBytes (índice 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;

público 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;

público 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;

público ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

público ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

GPUOptions públicas getGpuOptions ()

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

GPUOptionsOrBuilder público getGpuOptionsOrBuilder ()

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

GraphOptions públicas getGraphOptions ()

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

público GraphOptionsOrBuilder getGraphOptionsOrBuilder ()

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

público 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;

público 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;

getIsolateSessionState público booleano ()

 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 público booleano ()

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

público largo 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;

público getParserForType ()

público 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;

RPCOptions públicas getRpcOptions ()

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

público RPCOptionsOrBuilder getRpcOptionsOrBuilder ()

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

público int getSerializedSize ()

público ThreadPoolOptionPro para getSessionInterOpThreadPool (índice 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;

público 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;

Lista pública< 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;

público ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (índice 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;

Lista pública<? extiende 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;

público booleano 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;

público final com.google.protobuf.UnknownFieldSet getUnknownFields ()

getUsePerSessionThreads booleano público ()

 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;

hasClusterDef booleano público ()

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

hasExperimental público booleano ()

.tensorflow.ConfigProto.Experimental experimental = 16;

hasGpuOptions booleano público ()

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

hasGraphOptions booleano público ()

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

hasRpcOptions booleano público ()

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

código hash int público ()

público final booleano isInitialized ()

público estático ConfigProto.Builder newBuilder ()

público estático ConfigProto.Builder newBuilder (prototipo ConfigProto )

público ConfigProto.Builder newBuilderForType ()

ConfigProto estático público parseDelimitedFrom (entrada de InputStream)

Lanza
IOExcepción

ConfigProto estático público parseDelimitedFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

ConfigProto parseFrom estático público (datos ByteBuffer, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
Excepción de buffer de protocolo no válido

ConfigProto estático público parseFrom (entrada com.google.protobuf.CodedInputStream)

Lanza
IOExcepción

ConfigProto estático público parseFrom (byte[] datos, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
Excepción de buffer de protocolo no válido

ConfigProto estático público parseFrom (datos ByteBuffer)

Lanza
Excepción de buffer de protocolo no válido

ConfigProto parseFrom estático público (entrada com.google.protobuf.CodedInputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

ConfigProto estático público parseFrom (datos com.google.protobuf.ByteString)

Lanza
Excepción de buffer de protocolo no válido

ConfigProto estático público parseFrom (entrada de InputStream, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
IOExcepción

público estático ConfigProto parseFrom (datos com.google.protobuf.ByteString, com.google.protobuf.ExtensionRegistryLite extensionRegistry)

Lanza
Excepción de buffer de protocolo no válido

estática pública analizador ()

público ConfigProto.Builder toBuilder ()

escritura vacía pública (salida de com.google.protobuf.CodedOutputStream)

Lanza
IOExcepción