Package org.tensorflow.framework

Interface ConfigProtoOrBuilder

All Superinterfaces:
com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
All Known Implementing Classes:
ConfigProto, ConfigProto.Builder
public interface ConfigProtoOrBuilder extends com.google.protobuf.MessageOrBuilder

  • Method Details

    • getDeviceCountCount

      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;

    • containsDeviceCount

      boolean containsDeviceCount(String key)
       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;

    • getDeviceCount

      @Deprecated Map<String,Integer> getDeviceCount()
      Deprecated.
      Use getDeviceCountMap() instead.

    • getDeviceCountMap

      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;

    • getDeviceCountOrDefault

      int getDeviceCountOrDefault(String key, 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;

    • getDeviceCountOrThrow

      int getDeviceCountOrThrow(String key)
       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;

    • getIntraOpParallelismThreads

      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 described 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;
      Returns:
      The intraOpParallelismThreads.

    • getInterOpParallelismThreads

      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;
      Returns:
      The interOpParallelismThreads.

    • getUsePerSessionThreads

      boolean 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;
      Returns:
      The usePerSessionThreads.

    • getSessionInterOpThreadPoolList

      List<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;

    • getSessionInterOpThreadPool

      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;

    • getSessionInterOpThreadPoolCount

      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;

    • getSessionInterOpThreadPoolOrBuilderList

      List<? extends 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;

    • getSessionInterOpThreadPoolOrBuilder

      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;

    • getPlacementPeriod

      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;
      Returns:
      The placementPeriod.

    • getDeviceFiltersList

      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;
      Returns:
      A list containing the deviceFilters.

    • getDeviceFiltersCount

      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;
      Returns:
      The count of deviceFilters.

    • getDeviceFilters

      String 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;
      Parameters:
      index - The index of the element to return.
      Returns:
      The deviceFilters at the given index.

    • getDeviceFiltersBytes

      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;
      Parameters:
      index - The index of the value to return.
      Returns:
      The bytes of the deviceFilters at the given index.

    • hasGpuOptions

      boolean hasGpuOptions()
       Options that apply to all GPUs.
      .tensorflow.GPUOptions gpu_options = 6;
      Returns:
      Whether the gpuOptions field is set.

    • getGpuOptions

      GPUOptions getGpuOptions()
       Options that apply to all GPUs.
      .tensorflow.GPUOptions gpu_options = 6;
      Returns:
      The gpuOptions.

    • getGpuOptionsOrBuilder

      GPUOptionsOrBuilder getGpuOptionsOrBuilder()
       Options that apply to all GPUs.
      .tensorflow.GPUOptions gpu_options = 6;

    • hasPluggableDeviceOptions

      boolean hasPluggableDeviceOptions()
       Options that apply to pluggable devices.
      .tensorflow.GPUOptions pluggable_device_options = 18;
      Returns:
      Whether the pluggableDeviceOptions field is set.

    • getPluggableDeviceOptions

      GPUOptions getPluggableDeviceOptions()
       Options that apply to pluggable devices.
      .tensorflow.GPUOptions pluggable_device_options = 18;
      Returns:
      The pluggableDeviceOptions.

    • getPluggableDeviceOptionsOrBuilder

      GPUOptionsOrBuilder getPluggableDeviceOptionsOrBuilder()
       Options that apply to pluggable devices.
      .tensorflow.GPUOptions pluggable_device_options = 18;

    • getAllowSoftPlacement

      boolean 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;
      Returns:
      The allowSoftPlacement.

    • getLogDevicePlacement

      boolean getLogDevicePlacement()
       Whether device placements should be logged.
      bool log_device_placement = 8;
      Returns:
      The logDevicePlacement.

    • hasGraphOptions

      boolean hasGraphOptions()
       Options that apply to all graphs.
      .tensorflow.GraphOptions graph_options = 10;
      Returns:
      Whether the graphOptions field is set.

    • getGraphOptions

      GraphOptions getGraphOptions()
       Options that apply to all graphs.
      .tensorflow.GraphOptions graph_options = 10;
      Returns:
      The graphOptions.

    • getGraphOptionsOrBuilder

      GraphOptionsOrBuilder getGraphOptionsOrBuilder()
       Options that apply to all graphs.
      .tensorflow.GraphOptions graph_options = 10;

    • getOperationTimeoutInMs

      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;
      Returns:
      The operationTimeoutInMs.

    • hasRpcOptions

      boolean hasRpcOptions()
       Options that apply when this session uses the distributed runtime.
      .tensorflow.RPCOptions rpc_options = 13;
      Returns:
      Whether the rpcOptions field is set.

    • getRpcOptions

      RpcOptions.RPCOptions getRpcOptions()
       Options that apply when this session uses the distributed runtime.
      .tensorflow.RPCOptions rpc_options = 13;
      Returns:
      The rpcOptions.

    • getRpcOptionsOrBuilder

      RpcOptions.RPCOptionsOrBuilder getRpcOptionsOrBuilder()
       Options that apply when this session uses the distributed runtime.
      .tensorflow.RPCOptions rpc_options = 13;

    • hasClusterDef

      boolean hasClusterDef()
       Optional list of all workers to use in this session.
      .tensorflow.ClusterDef cluster_def = 14;
      Returns:
      Whether the clusterDef field is set.

    • getClusterDef

      ClusterDef getClusterDef()
       Optional list of all workers to use in this session.
      .tensorflow.ClusterDef cluster_def = 14;
      Returns:
      The clusterDef.

    • getClusterDefOrBuilder

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

    • getIsolateSessionState

      boolean 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;
      Returns:
      The isolateSessionState.

    • getShareClusterDevicesInSession

      boolean 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;
      Returns:
      The shareClusterDevicesInSession.

    • hasExperimental

      boolean hasExperimental()
      .tensorflow.ConfigProto.Experimental experimental = 16;
      Returns:
      Whether the experimental field is set.

    • getExperimental

      ConfigProto.Experimental getExperimental()
      .tensorflow.ConfigProto.Experimental experimental = 16;
      Returns:
      The experimental.

    • getExperimentalOrBuilder

      ConfigProto.ExperimentalOrBuilder getExperimentalOrBuilder()
      .tensorflow.ConfigProto.Experimental experimental = 16;