ממשק ציבורי ConfigProtoOrBuilder
| תת-מחלקות עקיפות ידועות |
שיטות ציבוריות
| בוליאני מופשט | containsDeviceCount (מפתח מחרוזת) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| בוליאני מופשט | getAllowSoftPlacement () Whether soft placement is allowed. |
| מופשט ClusterDef | getClusterDef () Optional list of all workers to use in this session. |
| תקציר ClusterDefOrBuilder | getClusterDefOrBuilder () Optional list of all workers to use in this session. |
| מפה מופשטת<String, Integer> | getDeviceCount () השתמש getDeviceCountMap() במקום זאת. |
| מופשט int | getDeviceCountCount () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| מפה מופשטת<String, Integer> | getDeviceCountMap () Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| מופשט int | getDeviceCountOrDefault (מפתח מחרוזת, int defaultValue) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| מופשט int | getDeviceCountOrThrow (מפתח מחרוזת) Map from device type name (e.g., "CPU" or "GPU" ) to maximum number of devices of that type to use. |
| מחרוזת מופשטת | getDeviceFilters (int index) When any filters are present sessions will ignore all devices which do not match the filters. |
| תקציר com.google.protobuf.ByteString | getDeviceFiltersBytes (int index) When any filters are present sessions will ignore all devices which do not match the filters. |
| מופשט int | getDeviceFiltersCount () When any filters are present sessions will ignore all devices which do not match the filters. |
| רשימה מופשטת<String> | getDeviceFiltersList () When any filters are present sessions will ignore all devices which do not match the filters. |
| תקציר ConfigProto.Experimental | getExperimental () .tensorflow.ConfigProto.Experimental experimental = 16; |
| תקציר ConfigProto.ExperimentalOrBuilder | getExperimentalOrBuilder () .tensorflow.ConfigProto.Experimental experimental = 16; |
| אבסטרקט GPUOptions | getGpuOptions () Options that apply to all GPUs. |
| מופשט GPUOptionsOrBuilder | getGpuOptionsOrBuilder () Options that apply to all GPUs. |
| מופשט GraphOptions | getGraphOptions () Options that apply to all graphs. |
| מופשט GraphOptionsOrBuilder | getGraphOptionsOrBuilder () Options that apply to all graphs. |
| מופשט int | getInterOpParallelismThreads () Nodes that perform blocking operations are enqueued on a pool of inter_op_parallelism_threads available in each process. |
| מופשט int | getIntraOpParallelismThreads () The execution of an individual op (for some op types) can be parallelized on a pool of intra_op_parallelism_threads. |
| בוליאני מופשט | getIsolateSessionState () If true, any resources such as Variables used in the session will not be shared with other sessions. |
| בוליאני מופשט | getLogDevicePlacement () Whether device placements should be logged. |
| מופשט ארוך | getOperationTimeoutInMs () Global timeout for all blocking operations in this session. |
| מופשט 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). |
| אבסטרקט RPCOptions | getRpcOptions () Options that apply when this session uses the distributed runtime. |
| תקציר RPCOptionsOrBuilder | getRpcOptionsOrBuilder () Options that apply when this session uses the distributed runtime. |
| מופשט ThreadPoolOptionProto | getSessionInterOpThreadPool (int index) This option is experimental - it may be replaced with a different mechanism in the future. |
| מופשט int | getSessionInterOpThreadPoolCount () This option is experimental - it may be replaced with a different mechanism in the future. |
| רשימה מופשטת< ThreadPoolOptionProto > | getSessionInterOpThreadPoolList () This option is experimental - it may be replaced with a different mechanism in the future. |
| תקציר ThreadPoolOptionProtoOrBuilder | getSessionInterOpThreadPoolOrBuilder (int index) This option is experimental - it may be replaced with a different mechanism in the future. |
| רשימה מופשטת<? מרחיב את ThreadPoolOptionProtoOrBuilder > | getSessionInterOpThreadPoolOrBuilderList () This option is experimental - it may be replaced with a different mechanism in the future. |
| בוליאני מופשט | getShareClusterDevicesInSession () When true, WorkerSessions are created with device attributes from the full cluster. |
| בוליאני מופשט | getUsePerSessionThreads () If true, use a new set of threads for this session rather than the global pool of threads. |
| בוליאני מופשט | hasClusterDef () Optional list of all workers to use in this session. |
| בוליאני מופשט | hasExperimental () .tensorflow.ConfigProto.Experimental experimental = 16; |
| בוליאני מופשט | hasGpuOptions () Options that apply to all GPUs. |
| בוליאני מופשט | hasGraphOptions () Options that apply to all graphs. |
| בוליאני מופשט | hasRpcOptions () Options that apply when this session uses the distributed runtime. |
שיטות ציבוריות
public abstract boolean containsDeviceCount (מפתח מחרוזת)
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; תקציר בוליאני ציבורי 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; תקציר ציבורי ClusterDef getClusterDef ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; תקציר ציבורי ClusterDefOrBuilder getClusterDefOrBuilder ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; תקציר ציבורי 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; תקציר ציבורי מפה<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 (מפתח מחרוזת, 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;תקציר ציבורי int getDeviceCountOrThrow (מפתח מחרוזת)
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; מחרוזת תקציר ציבורי getDeviceFilters (int index)
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; תקציר ציבורי com.google.protobuf.ByteString getDeviceFiltersBytes (int index)
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; תקציר ציבורי 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; רשימת תקציר ציבורית<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; תקציר ציבורי ConfigProto.Experimental getExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
תקציר ציבורי ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder ()
.tensorflow.ConfigProto.Experimental experimental = 16;
תקציר ציבורי GPUOptions getGpuOptions ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; תקציר ציבורי GPUOptionsOrBuilder getGpuOptionsOrBuilder ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; תקציר ציבורי GraphOptions getGraphOptions ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; תקציר ציבורי GraphOptionsOrBuilder getGraphOptionsOrBuilder ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; תקציר ציבורי 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; תקציר ציבורי 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; תקציר בוליאני ציבורי 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; תקציר בוליאני ציבורי getLogDevicePlacement ()
Whether device placements should be logged.
bool log_device_placement = 8; תקציר ציבורי ארוך 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; תקציר ציבורי 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; תקציר ציבורי RPCOptions getRpcOptions ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13; תקציר ציבורי RPCOptionsOrBuilder getRpcOptionsOrBuilder ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13; תקציר ציבורי ThreadPoolOptionProto getSessionInterOpThreadPool (int index)
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; תקציר ציבורי 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; רשימת תקציר ציבורית< 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; תקציר ציבורי ThreadPoolOptionProtoOrBuilder getSessionInterOpThreadPoolOrBuilder (int index)
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; רשימת תקציר ציבורית<? מרחיב את 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; תקציר בוליאני ציבורי 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; תקציר בוליאני ציבורי 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; תקציר בוליאני ציבורי hasClusterDef ()
Optional list of all workers to use in this session.
.tensorflow.ClusterDef cluster_def = 14; תקציר ציבורי בוליאני hasExperimental ()
.tensorflow.ConfigProto.Experimental experimental = 16;
תקציר ציבורי בוליאני hasGpuOptions ()
Options that apply to all GPUs.
.tensorflow.GPUOptions gpu_options = 6; תקציר ציבורי בוליאני hasGraphOptions ()
Options that apply to all graphs.
.tensorflow.GraphOptions graph_options = 10; תקציר בוליאני ציבורי hasRpcOptions ()
Options that apply when this session uses the distributed runtime.
.tensorflow.RPCOptions rpc_options = 13;