Package org.tensorflow.framework

Interface ConfigProto.ExperimentalOrBuilder

    • Method Detail

      • getCollectiveGroupLeader

        java.lang.String getCollectiveGroupLeader()
         Task name for group resolution.
        string collective_group_leader = 1;
        Returns:
        The collectiveGroupLeader.

      • getCollectiveGroupLeaderBytes

        com.google.protobuf.ByteString getCollectiveGroupLeaderBytes()
         Task name for group resolution.
        string collective_group_leader = 1;
        Returns:
        The bytes for collectiveGroupLeader.

      • getExecutorType

        java.lang.String getExecutorType()
         Which executor to use, the default executor will be used
 if it is an empty string or "DEFAULT"
        string executor_type = 3;
        Returns:
        The executorType.

      • getExecutorTypeBytes

        com.google.protobuf.ByteString getExecutorTypeBytes()
         Which executor to use, the default executor will be used
 if it is an empty string or "DEFAULT"
        string executor_type = 3;
        Returns:
        The bytes for executorType.

      • getRecvBufMaxChunk

        int getRecvBufMaxChunk()
         Guidance to formatting of large RecvBuf fields for transfer.
 Any positive value sets the max chunk size.  0 defaults to 4096.
 Any negative value indicates no max, i.e. one chunk only.
        int32 recv_buf_max_chunk = 4;
        Returns:
        The recvBufMaxChunk.

      • getUseNumaAffinity

        boolean getUseNumaAffinity()
         If true, and supported by the platform, the runtime will attempt to
 use NUMA affinity where applicable.  One consequence will be the
 existence of as many CPU devices as there are available NUMA nodes.
        bool use_numa_affinity = 5;
        Returns:
        The useNumaAffinity.

      • getCollectiveDeterministicSequentialExecution

        boolean getCollectiveDeterministicSequentialExecution()
         If true, make collective op execution order sequential and deterministic
 for potentially concurrent collective instances.
        bool collective_deterministic_sequential_execution = 6;
        Returns:
        The collectiveDeterministicSequentialExecution.

      • getCollectiveNccl

        boolean getCollectiveNccl()
         If true, use NCCL for CollectiveOps.  This feature is highly
 experimental.
        bool collective_nccl = 7;
        Returns:
        The collectiveNccl.

      • getShareSessionStateInClusterspecPropagation

        boolean getShareSessionStateInClusterspecPropagation()
         In the following, session state means the value of a variable, elements
 in a hash table, or any other resource, accessible by worker sessions
 held by a TF server.
 When ClusterSpec propagation is enabled, the value of
 isolate_session_state is ignored when deciding whether to share session
 states in a TF server (for backwards compatibility reasons).
 - If share_session_state_in_clusterspec_propagation is true, the session
 states are shared.
 - If share_session_state_in_clusterspec_propagation is false, session
 states are isolated.
 When clusterspec propagation is not used, the value of
 share_session_state_in_clusterspec_propagation is ignored when deciding
 whether to share session states in a TF server.
 - If isolate_session_state is true, session states are isolated.
 - If isolate_session_state is false, session states are shared.
 TODO(b/129330037): Add a single API that consistently treats
 isolate_session_state and ClusterSpec propagation.
        bool share_session_state_in_clusterspec_propagation = 8;
        Returns:
        The shareSessionStateInClusterspecPropagation.

      • getDisableThreadSpinning

        boolean getDisableThreadSpinning()
         If using a direct session, disable spinning while waiting for work in
 the thread pool. This may result in higher latency for completing ops,
 but in the case where there is a lot of spinning may result in lower
 CPU usage.
        bool disable_thread_spinning = 9;
        Returns:
        The disableThreadSpinning.

      • 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 = 10;
        Returns:
        The shareClusterDevicesInSession.

      • hasSessionMetadata

        boolean hasSessionMetadata()
         Metadata about the session.
 If set, this can be used by the runtime and the Ops for debugging,
 monitoring, etc.
 NOTE: This is currently used and propagated only by the direct session.
        .tensorflow.SessionMetadata session_metadata = 11;
        Returns:
        Whether the sessionMetadata field is set.

      • getSessionMetadata

        SessionMetadata getSessionMetadata()
         Metadata about the session.
 If set, this can be used by the runtime and the Ops for debugging,
 monitoring, etc.
 NOTE: This is currently used and propagated only by the direct session.
        .tensorflow.SessionMetadata session_metadata = 11;
        Returns:
        The sessionMetadata.

      • getSessionMetadataOrBuilder

        SessionMetadataOrBuilder getSessionMetadataOrBuilder()
         Metadata about the session.
 If set, this can be used by the runtime and the Ops for debugging,
 monitoring, etc.
 NOTE: This is currently used and propagated only by the direct session.
        .tensorflow.SessionMetadata session_metadata = 11;

      • getOptimizeForStaticGraph

        boolean getOptimizeForStaticGraph()
         If true, the session may treat the graph as being static for optimization
 purposes.
 If this option is set to true when a session is created, the full
 GraphDef must be passed in a single call to Session::Create(), and
 Session::Extend() may not be supported.
        bool optimize_for_static_graph = 12;
        Returns:
        The optimizeForStaticGraph.

      • getEnableMlirBridge

        boolean getEnableMlirBridge()
         Whether to enable the MLIR-based TF->XLA bridge.
 This is a replacement to the existing bridge, and not ready for
 production usage yet.
 If this option is set to true when a session is created, MLIR is used to
 perform the set of graph transformations to put the graph in a form that
 can be executed with delegation of some computations to an accelerator.
 This builds on the model of XLA where a subset of the graph is
 encapsulated and attached to a "compile" operation, whose result is fed
 to an "execute" operation. The kernel for these operations is responsible
 to lower the encapsulated graph to a particular device.
        bool enable_mlir_bridge = 13;
        Returns:
        The enableMlirBridge.

      • getDisableOutputPartitionGraphs

        boolean getDisableOutputPartitionGraphs()
         If true, the session will not store an additional copy of the graph for
 each subgraph.
 If this option is set to true when a session is created, the
 `RunOptions.output_partition_graphs` options must not be set.
        bool disable_output_partition_graphs = 14;
        Returns:
        The disableOutputPartitionGraphs.

      • getXlaFusionAutotunerThresh

        long getXlaFusionAutotunerThresh()
         Minimum number of batches run through the XLA graph before XLA fusion
 autotuner is enabled. Default value of zero disables the autotuner.
 The XLA fusion autotuner can improve performance by executing a heuristic
 search on the compiler parameters.
        int64 xla_fusion_autotuner_thresh = 15;
        Returns:
        The xlaFusionAutotunerThresh.