interfaz pública ConfigProtoOrBuilder
Subclases indirectas conocidas |
Métodos públicos
booleano abstracto | containsDeviceCount (clave de cadena) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
booleano abstracto | getAllowSoftPlacement () Whether soft placement is allowed. |
ClusterDef abstracto | getClusterDef () Optional list of all workers to use in this session. |
resumen ClusterDefOrBuilder | getClusterDefOrBuilder () Optional list of all workers to use in this session. |
Mapa abstracto <String, Integer> | getDeviceCount () En su lugar, utilice getDeviceCountMap() . |
int abstracto | getDeviceCountCount () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
Mapa abstracto <String, Integer> | getDeviceCountMap () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
int abstracto | getDeviceCountOrDefault (clave de cadena, int valor predeterminado) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
int abstracto | 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 abstracta | getDeviceFilters (índice int) When any filters are present sessions will ignore all devices which do not match the filters. |
resumen com.google.protobuf.ByteString | getDeviceFiltersBytes (índice int) When any filters are present sessions will ignore all devices which do not match the filters. |
int abstracto | getDeviceFiltersCount () When any filters are present sessions will ignore all devices which do not match the filters. |
Lista abstracta <String> | getDeviceFiltersList () When any filters are present sessions will ignore all devices which do not match the filters. |
ConfigProto.Experimental abstracto | getExperimental () .tensorflow.ConfigProto.Experimental experimental = 16; |
ConfigProto.ExperimentalOrBuilder abstracto | getExperimentalOrBuilder () .tensorflow.ConfigProto.Experimental experimental = 16; |
GPUOptions abstractas | getGpuOptions () Options that apply to all GPUs. |
GPUOptionsOrBuilder abstracto | getGpuOptionsOrBuilder () Options that apply to all GPUs. |
GraphOptions abstracto | getGraphOptions () Options that apply to all graphs. |
GraphOptionsOrBuilder abstracto | getGraphOptionsOrBuilder () Options that apply to all graphs. |
int abstracto | getInterOpParallelismThreads () Nodes that perform blocking operations are enqueued on a pool of inter_op_parallelism_threads available in each process. |
int abstracto | getIntraOpParallelismThreads () The execution of an individual op (for some op types) can be parallelized on a pool of intra_op_parallelism_threads. |
booleano abstracto | getIsolateSessionState () If true, any resources such as Variables used in the session will not be shared with other sessions. |
booleano abstracto | getLogDevicePlacement () Whether device placements should be logged. |
abstracto largo | getOperationTimeoutInMs () Global timeout for all blocking operations in this session. |
int abstracto | 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). |
RPCOpciones abstractas | getRpcOptions () Options that apply when this session uses the distributed runtime. |
RPCOptionsOrBuilder abstracto | getRpcOptionsOrBuilder () Options that apply when this session uses the distributed runtime. |
resumen ThreadPoolOptionProto | getSessionInterOpThreadPool (índice int) This option is experimental - it may be replaced with a different mechanism in the future. |
int abstracto | getSessionInterOpThreadPoolCount () This option is experimental - it may be replaced with a different mechanism in the future. |
Lista abstracta < ThreadPoolOptionProto > | getSessionInterOpThreadPoolList () This option is experimental - it may be replaced with a different mechanism in the future. |
resumen ThreadPoolOptionProtoOrBuilder | getSessionInterOpThreadPoolOrBuilder (índice int) This option is experimental - it may be replaced with a different mechanism in the future. |
Resumen Lista <? extiende ThreadPoolOptionProtoOrBuilder > | getSessionInterOpThreadPoolOrBuilderList () This option is experimental - it may be replaced with a different mechanism in the future. |
booleano abstracto | getShareClusterDevicesInSession () When true, WorkerSessions are created with device attributes from the full cluster. |
booleano abstracto | getUsePerSessionThreads () If true, use a new set of threads for this session rather than the global pool of threads. |
booleano abstracto | hasClusterDef () Optional list of all workers to use in this session. |
booleano abstracto | hasExperimental () .tensorflow.ConfigProto.Experimental experimental = 16; |
booleano abstracto | hasGpuOptions () Options that apply to all GPUs. |
booleano abstracto | hasGraphOptions () Options that apply to all graphs. |
booleano abstracto | hasRpcOptions () Options that apply when this session uses the distributed runtime. |
Métodos públicos
public abstract boolean containsDeviceCount (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;
getAllowSoftPlacement () booleano abstracto 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;
público abstracto ClusterDef getClusterDef ()
Optional list of all workers to use in this session..tensorflow.ClusterDef
.tensorflow.ClusterDef cluster_def = 14;
público abstracto ClusterDefOrBuilder getClusterDefOrBuilder ()
Optional list of all workers to use in this session..tensorflow.ClusterDef
.tensorflow.ClusterDef cluster_def = 14;
Mapa abstracto público <String, Integer> getDeviceCount ()
En su lugar, utilice 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;
Mapa abstracto público <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 (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 abstract 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;
getDeviceFilters de cadena abstracta pública (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;
resumen 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;
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;
Lista pública abstracta <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;
resumen público ConfigProto.Experimental getExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
resumen público ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
GPUOptions abstractas públicas getGpuOptions ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;
public abstract GPUOptionsOrBuilder getGpuOptionsOrBuilder ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;
GraphOptions abstracto público getGraphOptions ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;
GraphOptionsOrBuilder público abstracto 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;
getIsolateSessionState booleano abstracto público ()
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 () booleano abstracto público
Whether device placements should be logged.
bool log_device_placement = 8;
public abstract long 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
int32 placement_period = 3;
RPCOptions abstractas públicas getRpcOptions ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;
RPCOptionsOrBuilder público abstracto getRpcOptionsOrBuilder ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;
público abstracto ThreadPoolOptionProto 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..tensorflow.ThreadPoolOptionProto
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..tensorflow.ThreadPoolOptionProto
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
Lista pública abstracta < 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..tensorflow.ThreadPoolOptionProto
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
público abstracto 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..tensorflow.ThreadPoolOptionProto
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
Lista de resumen público <? 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..tensorflow.ThreadPoolOptionProto
repeated .tensorflow.ThreadPoolOptionProto session_inter_op_thread_pool = 12;
getShareClusterDevicesInSession () booleano abstracto público
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;
getUsePerSessionThreads booleano público abstracto ()
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;
público abstracto booleano hasClusterDef ()
Optional list of all workers to use in this session..tensorflow.ClusterDef
.tensorflow.ClusterDef cluster_def = 14;
public abstract boolean hasExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
hasGpuOptions () booleano abstracto público
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6;
hasGraphOptions () booleano abstracto público
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10;
hasRpcOptions () booleano abstracto público
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;