ConfigProtoOrBuilder

interfaccia pubblica ConfigProtoOrBuilder
Sottoclassi indirette conosciute

Metodi pubblici

booleano astratto
contieneDeviceCount (chiave String)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
booleano astratto
getAllowSoftPlacement ()
 Whether soft placement is allowed.
astratto ClusterDef
getClusterDef ()
 Optional list of all workers to use in this session.
astratto ClusterDefOrBuilder
getClusterDefOrBuilder ()
 Optional list of all workers to use in this session.
mappa astratta<String, Integer>
getDeviceCount ()
Utilizzare invece getDeviceCountMap() .
astratto int
getDeviceCountCount ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
mappa astratta<String, Integer>
getDeviceCountMap ()
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
astratto int
getDeviceCountOrDefault (chiave String, int defaultValue)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
astratto int
getDeviceCountOrThrow (chiave String)
 Map from device type name (e.g., "CPU" or "GPU" ) to maximum
 number of devices of that type to use.
stringa astratta
getDeviceFilters (indice int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
astratto com.google.protobuf.ByteString
getDeviceFiltersBytes (indice int)
 When any filters are present sessions will ignore all devices which do not
 match the filters.
astratto int
getDeviceFiltersCount ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
Elenco astratto<String>
getDeviceFiltersList ()
 When any filters are present sessions will ignore all devices which do not
 match the filters.
abstract ConfigProto.Experimental
getExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
abstract ConfigProto.ExperimentalOrBuilder
getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
Opzioni GPU astratte
getGpuOptions ()
 Options that apply to all GPUs.
GPUOptionsOrBuilder astratto
getGpuOptionsOrBuilder ()
 Options that apply to all GPUs.
OpzioniGrafico astratte
getOpzioniGrafico ()
 Options that apply to all graphs.
astratto GraphOptionsOrBuilder
getOpzioniGraphOrBuilder ()
 Options that apply to all graphs.
astratto int
getInterOpParallelismThreads ()
 Nodes that perform blocking operations are enqueued on a pool of
 inter_op_parallelism_threads available in each process.
astratto int
getIntraOpParallelismThreads ()
 The execution of an individual op (for some op types) can be
 parallelized on a pool of intra_op_parallelism_threads.
booleano astratto
getIsolateSessionState ()
 If true, any resources such as Variables used in the session will not be
 shared with other sessions.
booleano astratto
getLogDevicePlacement ()
 Whether device placements should be logged.
astratto lungo
getOperationTimeoutInMs ()
 Global timeout for all blocking operations in this session.
astratto 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).
Opzioni RPC astratte
getRpcOptions ()
 Options that apply when this session uses the distributed runtime.
RPCOptionsOrBuilder astratto
getRpcOptionsOrBuilder ()
 Options that apply when this session uses the distributed runtime.
astratto ThreadPoolOptionProto
getSessionInterOpThreadPool (indice int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
astratto int
getSessionInterOpThreadPoolCount ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Elenco astratto< ThreadPoolOptionProto >
getSessionInterOpThreadPoolList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
astratto ThreadPoolOptionProtoOrBuilder
getSessionInterOpThreadPoolOrBuilder (indice int)
 This option is experimental - it may be replaced with a different mechanism
 in the future.
Elenco astratto<? estende ThreadPoolOptionProtoOrBuilder >
getSessionInterOpThreadPoolOrBuilderList ()
 This option is experimental - it may be replaced with a different mechanism
 in the future.
booleano astratto
getShareClusterDevicesInSession ()
 When true, WorkerSessions are created with device attributes from the
 full cluster.
booleano astratto
getUsePerSessionThreads ()
 If true, use a new set of threads for this session rather than the global
 pool of threads.
booleano astratto
hasClusterDef ()
 Optional list of all workers to use in this session.
booleano astratto
ha sperimentale ()
.tensorflow.ConfigProto.Experimental experimental = 16;
booleano astratto
hasGpuOptions ()
 Options that apply to all GPUs.
booleano astratto
haOpzioniGrafico ()
 Options that apply to all graphs.
booleano astratto
hasRpcOptions ()
 Options that apply when this session uses the distributed runtime.

Metodi pubblici

booleano astratto pubblico contieneDeviceCount (chiave 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;

pubblico astratto booleano 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;

estratto pubblico ClusterDef getClusterDef ()

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

estratto pubblico ClusterDefOrBuilder getClusterDefOrBuilder ()

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

public abstract Map<String, Integer> getDeviceCount ()

Utilizzare invece getDeviceCountMap() .

public abstract 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;

public abstract 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;

public abstract int getDeviceCountOrDefault (chiave 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 abstract int getDeviceCountOrThrow (chiave 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 abstract String getDeviceFilters (indice 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;

abstract pubblico com.google.protobuf.ByteString getDeviceFiltersBytes (indice 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;

public abstract int getDeviceFiltersCount ()

 When any filters are present sessions will ignore all devices which do not
 match the filters. Each filter can be partially specified, e.g. "/job:ps"
 "/job:worker/replica:3", etc.
 
repeated string device_filters = 4;

public abstract List<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;

public abstract ConfigProto.Experimental getExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

public abstract ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()

.tensorflow.ConfigProto.Experimental experimental = 16;

abstract pubblico GPUOptions getGpuOptions ()

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

estratto pubblico GPUOptionsOrBuilder getGpuOptionsOrBuilder ()

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

estratto pubblico GraphOptions getGraphOptions ()

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

estratto pubblico 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;

pubblico astratto booleano 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;

pubblico astratto booleano getLogDevicePlacement ()

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

pubblico astratto lungo getOperationTimeoutInMs ()

 Global timeout for all blocking operations in this session.  If non-zero,
 and not overridden on a per-operation basis, this value will be used as the
 deadline for all blocking operations.
 
int64 operation_timeout_in_ms = 11;

public abstract 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;

public abstract RPCOptions getRpcOptions ()

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

public abstract RPCOptionsOrBuilder getRpcOptionsOrBuilder ()

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

public abstract ThreadPoolOptionProto getSessionInterOpThreadPool (indice 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;

public abstract 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;

elenco astratto pubblico < 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;

public abstract ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (indice 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;

Elenco abstract pubblico<? estende 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;

pubblico astratto 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;

pubblico astratto booleano 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;

public abstract booleano hasClusterDef ()

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

public abstract booleano hasExperimental ()

.tensorflow.ConfigProto.Experimental experimental = 16;

hasGpuOptions booleano astratto pubblico ()

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

hasGraphOptions booleano astratto pubblico ()

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

hasRpcOptions booleano astratto pubblico ()

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