الواجهة العامة ConfigProtoOrBuilder
| الفئات الفرعية غير المباشرة المعروفة |
الأساليب العامة
| منطقية مجردة | يحتوي علىDeviceCount (مفتاح السلسلة) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| منطقية مجردة | الحصول علىAllowSoftPlacement () Whether soft placement is allowed. |
| مجردة ClusterDef | الحصول على ClusterDef () Optional list of all workers to use in this session. |
| مجردة ClusterDefOrBuilder | الحصول على ClusterDefOrBuilder () Optional list of all workers to use in this session. |
| خريطة مجردة <سلسلة، عدد صحيح> | getDeviceCount () استخدم getDeviceCountMap() بدلاً من ذلك. |
| كثافة العمليات مجردة | getDeviceCountCount () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| خريطة مجردة <سلسلة، عدد صحيح> | getDeviceCountMap () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| كثافة العمليات مجردة | getDeviceCountOrDefault (مفتاح السلسلة، int defaultValue) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| كثافة العمليات مجردة | getDeviceCountOrThrow (مفتاح السلسلة) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| سلسلة مجردة | getDeviceFilters (فهرس كثافة العمليات) When any filters are present sessions will ignore all devices which do not match the filters. |
| مجردة com.google.protobuf.ByteString | getDeviceFiltersBytes (فهرس كثافة العمليات) When any filters are present sessions will ignore all devices which do not match the filters. |
| كثافة العمليات مجردة | getDeviceFiltersCount () When any filters are present sessions will ignore all devices which do not match the filters. |
| قائمة مجردة <سلسلة> | getDeviceFiltersList () When any filters are present sessions will ignore all devices which do not match the filters. |
| مجردة ConfigProto.Experimental | الحصول التجريبي () .tensorflow.ConfigProto.Experimental experimental = 16; |
| مجردة ConfigProto.ExperimentalOrBuilder | الحصول على التجريبية أو البناء () .tensorflow.ConfigProto.Experimental experimental = 16; |
| خيارات GPU مجردة | خيارات getGpu () Options that apply to all GPUs. |
| مجردة GPUOptionsOrBuilder | getGpuOptionsOrBuilder () Options that apply to all GPUs. |
| خيارات الرسم البياني مجردة | خيارات الرسم البياني () Options that apply to all graphs. |
| مجردة GraphOptionsOrBuilder | الحصول على GraphOptionsOrBuilder () Options that apply to all graphs. |
| كثافة العمليات مجردة | getInterOpParallelismThreads () Nodes that perform blocking operations are enqueued on a pool of inter_op_parallelism_threads available in each process. |
| كثافة العمليات مجردة | getIntraOpParallelismThreads () The execution of an individual op (for some op types) can be parallelized on a pool of intra_op_parallelism_threads. |
| منطقية مجردة | getIsolateSessionState () If true, any resources such as Variables used in the session will not be shared with other sessions. |
| منطقية مجردة | getLogDevicePlacement () Whether device placements should be logged. |
| مجردة طويلة | الحصول علىOperationTimeoutInMs () Global timeout for all blocking operations in this session. |
| كثافة العمليات مجردة | الحصول على فترة التنسيب () 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). |
| خيارات RPC مجردة | خيارات الحصول على Rpc () Options that apply when this session uses the distributed runtime. |
| مجردة RPCOptionsOrBuilder | getRpcOptionsOrBuilder () Options that apply when this session uses the distributed runtime. |
| مجردة ThreadPoolOptionProto | getSessionInterOpThreadPool (فهرس كثافة العمليات) This option is experimental - it may be replaced with a different mechanism in the future. |
| كثافة العمليات مجردة | getSessionInterOpThreadPoolCount () This option is experimental - it may be replaced with a different mechanism in the future. |
| قائمة مجردة< ThreadPoolOptionProto > | getSessionInterOpThreadPoolList () This option is experimental - it may be replaced with a different mechanism in the future. |
| مجردة ThreadPoolOptionProtoOrBuilder | getSessionInterOpThreadPoolOrBuilder (فهرس كثافة العمليات) This option is experimental - it may be replaced with a different mechanism in the future. |
| قائمة مجردة <؟ يمتد ThreadPoolOptionProtoOrBuilder > | getSessionInterOpThreadPoolOrBuilderList () This option is experimental - it may be replaced with a different mechanism in the future. |
| منطقية مجردة | getShareClusterDevicesInSession () When true, WorkerSessions are created with device attributes from the full cluster. |
| منطقية مجردة | getUsePerSessionThreads () If true, use a new set of threads for this session rather than the global pool of threads. |
| منطقية مجردة | hasClusterDef () Optional list of all workers to use in this session. |
| منطقية مجردة | تجريبي () .tensorflow.ConfigProto.Experimental experimental = 16; |
| منطقية مجردة | hasGpuOptions () Options that apply to all GPUs. |
| منطقية مجردة | hasGraphOptions () Options that apply to all graphs. |
| منطقية مجردة | hasRpcOptions () Options that apply when this session uses the distributed runtime. |
الأساليب العامة
يحتوي الملخص المنطقي العام علىDeviceCount (مفتاح السلسلة)
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; الملخص المنطقي العام 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; الملخص العام ClusterDef getClusterDef ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; الملخص العام ClusterDefOrBuilder getClusterDefOrBuilder ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; الملخص العام 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; خريطة مجردة عامة <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; الملخص العام int getDeviceCountOrDefault (مفتاح السلسلة، 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;الملخص العام int getDeviceCountOrThrow (مفتاح السلسلة)
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; سلسلة getDeviceFilters العامة المجردة (مؤشر 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; الملخص العام com.google.protobuf.ByteString getDeviceFiltersBytes (مؤشر 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; الملخص العام 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; قائمة مجردة عامة<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; الملخص العام ConfigProto.Experimental getExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
الملخص العام ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
الملخص العام لـ GPUOptions getGpuOptions ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; الملخص العام GPUOptionsOrBuilder getGpuOptionsOrBuilder ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; الملخص العام GraphOptions getGraphOptions ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; الملخص العام GraphOptionsOrBuilder getGraphOptionsOrBuilder ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; الملخص العام 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; الملخص العام 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; الملخص المنطقي العام 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; getLogDevicePlacement () المنطقية العامة المجردة
Whether device placements should be logged.
bool log_device_placement = 8; ملخص عام طويل 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; الملخص العام 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; الملخص العام RPCOptions getRpcOptions ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13; الملخص العام RPCOptionsOrBuilder getRpcOptionsOrBuilder ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13; الملخص العام ThreadPoolOptionProto getSessionInterOpThreadPool (مؤشر 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; الملخص العام 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; قائمة مجردة عامة < 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; الملخص العام ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (مؤشر 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; قائمة الملخصات العامة<؟ يمتد 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; الملخص المنطقي العام 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; الملخص المنطقي العام 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; الملخص المنطقي العام hasClusterDef ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; الملخص المنطقي العام التجريبي ()
.tensorflow.ConfigProto.Experimental experimental = 16;
الملخص المنطقي العام hasGpuOptions ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; الملخص المنطقي العام hasGraphOptions ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; الملخص المنطقي العام hasRpcOptions ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;