Package org.tensorflow.framework

Class GPUOptions.Experimental

java.lang.Object
com.google.protobuf.AbstractMessageLite
com.google.protobuf.AbstractMessage
com.google.protobuf.GeneratedMessageV3
org.tensorflow.framework.GPUOptions.Experimental
All Implemented Interfaces:
com.google.protobuf.Message, com.google.protobuf.MessageLite, com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder, Serializable, GPUOptions.ExperimentalOrBuilder
Enclosing class:
GPUOptions
public static final class GPUOptions.Experimental extends com.google.protobuf.GeneratedMessageV3 implements GPUOptions.ExperimentalOrBuilder
Protobuf type tensorflow.GPUOptions.Experimental
See Also:

  • Field Details

    • VIRTUAL_DEVICES_FIELD_NUMBER

      public static final int VIRTUAL_DEVICES_FIELD_NUMBER
      See Also:

    • NUM_VIRTUAL_DEVICES_PER_GPU_FIELD_NUMBER

      public static final int NUM_VIRTUAL_DEVICES_PER_GPU_FIELD_NUMBER
      See Also:

    • USE_UNIFIED_MEMORY_FIELD_NUMBER

      public static final int USE_UNIFIED_MEMORY_FIELD_NUMBER
      See Also:

    • NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER

      public static final int NUM_DEV_TO_DEV_COPY_STREAMS_FIELD_NUMBER
      See Also:

    • COLLECTIVE_RING_ORDER_FIELD_NUMBER

      public static final int COLLECTIVE_RING_ORDER_FIELD_NUMBER
      See Also:

    • TIMESTAMPED_ALLOCATOR_FIELD_NUMBER

      public static final int TIMESTAMPED_ALLOCATOR_FIELD_NUMBER
      See Also:

    • KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER

      public static final int KERNEL_TRACKER_MAX_INTERVAL_FIELD_NUMBER
      See Also:

    • KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER

      public static final int KERNEL_TRACKER_MAX_BYTES_FIELD_NUMBER
      See Also:

    • KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER

      public static final int KERNEL_TRACKER_MAX_PENDING_FIELD_NUMBER
      See Also:

    • INTERNAL_FRAGMENTATION_FRACTION_FIELD_NUMBER

      public static final int INTERNAL_FRAGMENTATION_FRACTION_FIELD_NUMBER
      See Also:

    • USE_CUDA_MALLOC_ASYNC_FIELD_NUMBER

      public static final int USE_CUDA_MALLOC_ASYNC_FIELD_NUMBER
      See Also:

    • DISALLOW_RETRY_ON_ALLOCATION_FAILURE_FIELD_NUMBER

      public static final int DISALLOW_RETRY_ON_ALLOCATION_FAILURE_FIELD_NUMBER
      See Also:

    • GPU_HOST_MEM_LIMIT_IN_MB_FIELD_NUMBER

      public static final int GPU_HOST_MEM_LIMIT_IN_MB_FIELD_NUMBER
      See Also:

    • GPU_HOST_MEM_DISALLOW_GROWTH_FIELD_NUMBER

      public static final int GPU_HOST_MEM_DISALLOW_GROWTH_FIELD_NUMBER
      See Also:

    • GPU_SYSTEM_MEMORY_SIZE_IN_MB_FIELD_NUMBER

      public static final int GPU_SYSTEM_MEMORY_SIZE_IN_MB_FIELD_NUMBER
      See Also:

    • POPULATE_PJRT_GPU_CLIENT_CREATION_INFO_FIELD_NUMBER

      public static final int POPULATE_PJRT_GPU_CLIENT_CREATION_INFO_FIELD_NUMBER
      See Also:

    • NODE_ID_FIELD_NUMBER

      public static final int NODE_ID_FIELD_NUMBER
      See Also:

    • STREAM_MERGE_OPTIONS_FIELD_NUMBER

      public static final int STREAM_MERGE_OPTIONS_FIELD_NUMBER
      See Also:

  • Method Details

    • newInstance

      protected Object newInstance(com.google.protobuf.GeneratedMessageV3.UnusedPrivateParameter unused)
      Overrides:
      newInstance in class com.google.protobuf.GeneratedMessageV3

    • getDescriptor

      public static final com.google.protobuf.Descriptors.Descriptor getDescriptor()

    • internalGetFieldAccessorTable

      protected com.google.protobuf.GeneratedMessageV3.FieldAccessorTable internalGetFieldAccessorTable()
      Specified by:
      internalGetFieldAccessorTable in class com.google.protobuf.GeneratedMessageV3

    • getVirtualDevicesList

      public List<GPUOptions.Experimental.VirtualDevices> getVirtualDevicesList()
       The multi virtual device settings. If empty (not set), it will create
 single virtual device on each visible GPU, according to the settings
 in "visible_device_list" above. Otherwise, the number of elements in the
 list must be the same as the number of visible GPUs (after
 "visible_device_list" filtering if it is set), and the string represented
 device names (e.g. /device:GPU:<id>) will refer to the virtual
 devices and have the <id> field assigned sequentially starting from 0,
 according to the order of the virtual devices determined by
 device_ordinal and the location in the virtual device list.

 For example,
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB }
   virtual_devices { memory_limit: 3GB memory_limit: 4GB }
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory
   /device:GPU:1 -> visible GPU 1 with 2GB memory
   /device:GPU:2 -> visible GPU 0 with 3GB memory
   /device:GPU:3 -> visible GPU 0 with 4GB memory

 but
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB
                     device_ordinal: 10 device_ordinal: 20}
   virtual_devices { memory_limit: 3GB memory_limit: 4GB
                     device_ordinal: 10 device_ordinal: 20}
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory  (ordinal 10)
   /device:GPU:1 -> visible GPU 0 with 3GB memory  (ordinal 10)
   /device:GPU:2 -> visible GPU 1 with 2GB memory  (ordinal 20)
   /device:GPU:3 -> visible GPU 0 with 4GB memory  (ordinal 20)

 NOTE:
 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
    at the same time.
 2. Currently this setting is per-process, not per-session. Using
    different settings in different sessions within same process will
    result in undefined behavior.
      repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
      Specified by:
      getVirtualDevicesList in interface GPUOptions.ExperimentalOrBuilder

    • getVirtualDevicesOrBuilderList

      public List<? extends GPUOptions.Experimental.VirtualDevicesOrBuilder> getVirtualDevicesOrBuilderList()
       The multi virtual device settings. If empty (not set), it will create
 single virtual device on each visible GPU, according to the settings
 in "visible_device_list" above. Otherwise, the number of elements in the
 list must be the same as the number of visible GPUs (after
 "visible_device_list" filtering if it is set), and the string represented
 device names (e.g. /device:GPU:<id>) will refer to the virtual
 devices and have the <id> field assigned sequentially starting from 0,
 according to the order of the virtual devices determined by
 device_ordinal and the location in the virtual device list.

 For example,
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB }
   virtual_devices { memory_limit: 3GB memory_limit: 4GB }
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory
   /device:GPU:1 -> visible GPU 1 with 2GB memory
   /device:GPU:2 -> visible GPU 0 with 3GB memory
   /device:GPU:3 -> visible GPU 0 with 4GB memory

 but
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB
                     device_ordinal: 10 device_ordinal: 20}
   virtual_devices { memory_limit: 3GB memory_limit: 4GB
                     device_ordinal: 10 device_ordinal: 20}
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory  (ordinal 10)
   /device:GPU:1 -> visible GPU 0 with 3GB memory  (ordinal 10)
   /device:GPU:2 -> visible GPU 1 with 2GB memory  (ordinal 20)
   /device:GPU:3 -> visible GPU 0 with 4GB memory  (ordinal 20)

 NOTE:
 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
    at the same time.
 2. Currently this setting is per-process, not per-session. Using
    different settings in different sessions within same process will
    result in undefined behavior.
      repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
      Specified by:
      getVirtualDevicesOrBuilderList in interface GPUOptions.ExperimentalOrBuilder

    • getVirtualDevicesCount

      public int getVirtualDevicesCount()
       The multi virtual device settings. If empty (not set), it will create
 single virtual device on each visible GPU, according to the settings
 in "visible_device_list" above. Otherwise, the number of elements in the
 list must be the same as the number of visible GPUs (after
 "visible_device_list" filtering if it is set), and the string represented
 device names (e.g. /device:GPU:<id>) will refer to the virtual
 devices and have the <id> field assigned sequentially starting from 0,
 according to the order of the virtual devices determined by
 device_ordinal and the location in the virtual device list.

 For example,
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB }
   virtual_devices { memory_limit: 3GB memory_limit: 4GB }
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory
   /device:GPU:1 -> visible GPU 1 with 2GB memory
   /device:GPU:2 -> visible GPU 0 with 3GB memory
   /device:GPU:3 -> visible GPU 0 with 4GB memory

 but
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB
                     device_ordinal: 10 device_ordinal: 20}
   virtual_devices { memory_limit: 3GB memory_limit: 4GB
                     device_ordinal: 10 device_ordinal: 20}
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory  (ordinal 10)
   /device:GPU:1 -> visible GPU 0 with 3GB memory  (ordinal 10)
   /device:GPU:2 -> visible GPU 1 with 2GB memory  (ordinal 20)
   /device:GPU:3 -> visible GPU 0 with 4GB memory  (ordinal 20)

 NOTE:
 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
    at the same time.
 2. Currently this setting is per-process, not per-session. Using
    different settings in different sessions within same process will
    result in undefined behavior.
      repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
      Specified by:
      getVirtualDevicesCount in interface GPUOptions.ExperimentalOrBuilder

    • getVirtualDevices

      public GPUOptions.Experimental.VirtualDevices getVirtualDevices(int index)
       The multi virtual device settings. If empty (not set), it will create
 single virtual device on each visible GPU, according to the settings
 in "visible_device_list" above. Otherwise, the number of elements in the
 list must be the same as the number of visible GPUs (after
 "visible_device_list" filtering if it is set), and the string represented
 device names (e.g. /device:GPU:<id>) will refer to the virtual
 devices and have the <id> field assigned sequentially starting from 0,
 according to the order of the virtual devices determined by
 device_ordinal and the location in the virtual device list.

 For example,
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB }
   virtual_devices { memory_limit: 3GB memory_limit: 4GB }
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory
   /device:GPU:1 -> visible GPU 1 with 2GB memory
   /device:GPU:2 -> visible GPU 0 with 3GB memory
   /device:GPU:3 -> visible GPU 0 with 4GB memory

 but
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB
                     device_ordinal: 10 device_ordinal: 20}
   virtual_devices { memory_limit: 3GB memory_limit: 4GB
                     device_ordinal: 10 device_ordinal: 20}
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory  (ordinal 10)
   /device:GPU:1 -> visible GPU 0 with 3GB memory  (ordinal 10)
   /device:GPU:2 -> visible GPU 1 with 2GB memory  (ordinal 20)
   /device:GPU:3 -> visible GPU 0 with 4GB memory  (ordinal 20)

 NOTE:
 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
    at the same time.
 2. Currently this setting is per-process, not per-session. Using
    different settings in different sessions within same process will
    result in undefined behavior.
      repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
      Specified by:
      getVirtualDevices in interface GPUOptions.ExperimentalOrBuilder

    • getVirtualDevicesOrBuilder

      public GPUOptions.Experimental.VirtualDevicesOrBuilder getVirtualDevicesOrBuilder(int index)
       The multi virtual device settings. If empty (not set), it will create
 single virtual device on each visible GPU, according to the settings
 in "visible_device_list" above. Otherwise, the number of elements in the
 list must be the same as the number of visible GPUs (after
 "visible_device_list" filtering if it is set), and the string represented
 device names (e.g. /device:GPU:<id>) will refer to the virtual
 devices and have the <id> field assigned sequentially starting from 0,
 according to the order of the virtual devices determined by
 device_ordinal and the location in the virtual device list.

 For example,
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB }
   virtual_devices { memory_limit: 3GB memory_limit: 4GB }
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory
   /device:GPU:1 -> visible GPU 1 with 2GB memory
   /device:GPU:2 -> visible GPU 0 with 3GB memory
   /device:GPU:3 -> visible GPU 0 with 4GB memory

 but
   visible_device_list = "1,0"
   virtual_devices { memory_limit: 1GB memory_limit: 2GB
                     device_ordinal: 10 device_ordinal: 20}
   virtual_devices { memory_limit: 3GB memory_limit: 4GB
                     device_ordinal: 10 device_ordinal: 20}
 will create 4 virtual devices as:
   /device:GPU:0 -> visible GPU 1 with 1GB memory  (ordinal 10)
   /device:GPU:1 -> visible GPU 0 with 3GB memory  (ordinal 10)
   /device:GPU:2 -> visible GPU 1 with 2GB memory  (ordinal 20)
   /device:GPU:3 -> visible GPU 0 with 4GB memory  (ordinal 20)

 NOTE:
 1. It's invalid to set both this and "per_process_gpu_memory_fraction"
    at the same time.
 2. Currently this setting is per-process, not per-session. Using
    different settings in different sessions within same process will
    result in undefined behavior.
      repeated .tensorflow.GPUOptions.Experimental.VirtualDevices virtual_devices = 1;
      Specified by:
      getVirtualDevicesOrBuilder in interface GPUOptions.ExperimentalOrBuilder

    • getNumVirtualDevicesPerGpu

      public int getNumVirtualDevicesPerGpu()
       The number of virtual devices to create on each visible GPU. The
 available memory will be split equally among all virtual devices. If the
 field `memory_limit_mb` in `VirtualDevices` is not empty, this field will
 be ignored.
      int32 num_virtual_devices_per_gpu = 15;
      Specified by:
      getNumVirtualDevicesPerGpu in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The numVirtualDevicesPerGpu.

    • getUseUnifiedMemory

      public boolean getUseUnifiedMemory()
       If true, uses CUDA unified memory for memory allocations. If
 per_process_gpu_memory_fraction option is greater than 1.0, then unified
 memory is used regardless of the value for this field. See comments for
 per_process_gpu_memory_fraction field for more details and requirements
 of the unified memory. This option is useful to oversubscribe memory if
 multiple processes are sharing a single GPU while individually using less
 than 1.0 per process memory fraction.
      bool use_unified_memory = 2;
      Specified by:
      getUseUnifiedMemory in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The useUnifiedMemory.

    • getNumDevToDevCopyStreams

      public int getNumDevToDevCopyStreams()
       If > 1, the number of device-to-device copy streams to create
 for each GPUDevice.  Default value is 0, which is automatically
 converted to 1.
      int32 num_dev_to_dev_copy_streams = 3;
      Specified by:
      getNumDevToDevCopyStreams in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The numDevToDevCopyStreams.

    • getCollectiveRingOrder

      public String getCollectiveRingOrder()
       If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.  This assumes that all workers have the same GPU
 topology.  Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4".
 This ring order is used by the RingReducer implementation of
 CollectiveReduce, and serves as an override to automatic ring order
 generation in OrderTaskDeviceMap() during CollectiveParam resolution.
      string collective_ring_order = 4;
      Specified by:
      getCollectiveRingOrder in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The collectiveRingOrder.

    • getCollectiveRingOrderBytes

      public com.google.protobuf.ByteString getCollectiveRingOrderBytes()
       If non-empty, defines a good GPU ring order on a single worker based on
 device interconnect.  This assumes that all workers have the same GPU
 topology.  Specify as a comma-separated string, e.g. "3,2,1,0,7,6,5,4".
 This ring order is used by the RingReducer implementation of
 CollectiveReduce, and serves as an override to automatic ring order
 generation in OrderTaskDeviceMap() during CollectiveParam resolution.
      string collective_ring_order = 4;
      Specified by:
      getCollectiveRingOrderBytes in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The bytes for collectiveRingOrder.

    • getTimestampedAllocator

      public boolean getTimestampedAllocator()
       If true then extra work is done by GPUDevice and GPUBFCAllocator to
 keep track of when GPU memory is freed and when kernels actually
 complete so that we can know when a nominally free memory chunk
 is really not subject to pending use.
      bool timestamped_allocator = 5;
      Specified by:
      getTimestampedAllocator in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The timestampedAllocator.

    • getKernelTrackerMaxInterval

      public int getKernelTrackerMaxInterval()
       Parameters for GPUKernelTracker.  By default no kernel tracking is done.
 Note that timestamped_allocator is only effective if some tracking is
 specified.

 If kernel_tracker_max_interval = n > 0, then a tracking event
 is inserted after every n kernels without an event.
      int32 kernel_tracker_max_interval = 7;
      Specified by:
      getKernelTrackerMaxInterval in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The kernelTrackerMaxInterval.

    • getKernelTrackerMaxBytes

      public int getKernelTrackerMaxBytes()
       If kernel_tracker_max_bytes = n > 0, then a tracking event is
 inserted after every series of kernels allocating a sum of
 memory >= n.  If one kernel allocates b * n bytes, then one
 event will be inserted after it, but it will count as b against
 the pending limit.
      int32 kernel_tracker_max_bytes = 8;
      Specified by:
      getKernelTrackerMaxBytes in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The kernelTrackerMaxBytes.

    • getKernelTrackerMaxPending

      public int getKernelTrackerMaxPending()
       If kernel_tracker_max_pending > 0 then no more than this many
 tracking events can be outstanding at a time.  An attempt to
 launch an additional kernel will stall until an event
 completes.
      int32 kernel_tracker_max_pending = 9;
      Specified by:
      getKernelTrackerMaxPending in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The kernelTrackerMaxPending.

    • getInternalFragmentationFraction

      public double getInternalFragmentationFraction()
       BFC Allocator can return an allocated chunk of memory upto 2x the
 requested size. For virtual devices with tight memory constraints, and
 proportionately large allocation requests, this can lead to a significant
 reduction in available memory. The threshold below controls when a chunk
 should be split if the chunk size exceeds requested memory size. It is
 expressed as a fraction of total available memory for the tf device. For
 example setting it to 0.05 would imply a chunk needs to be split if its
 size exceeds the requested memory by 5% of the total virtual device/gpu
 memory size.
      double internal_fragmentation_fraction = 10;
      Specified by:
      getInternalFragmentationFraction in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The internalFragmentationFraction.

    • getUseCudaMallocAsync

      public boolean getUseCudaMallocAsync()
       When true, use CUDA cudaMallocAsync API instead of TF gpu allocator.
      bool use_cuda_malloc_async = 11;
      Specified by:
      getUseCudaMallocAsync in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The useCudaMallocAsync.

    • getDisallowRetryOnAllocationFailure

      public boolean getDisallowRetryOnAllocationFailure()
       By default, BFCAllocator may sleep when it runs out of memory, in the
 hopes that another thread will free up memory in the meantime.  Setting
 this to true disables the sleep; instead we'll OOM immediately.
      bool disallow_retry_on_allocation_failure = 12;
      Specified by:
      getDisallowRetryOnAllocationFailure in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The disallowRetryOnAllocationFailure.

    • getGpuHostMemLimitInMb

      public float getGpuHostMemLimitInMb()
       Memory limit for "GPU host allocator", aka pinned memory allocator.  This
 can also be set via the envvar TF_GPU_HOST_MEM_LIMIT_IN_MB.
      float gpu_host_mem_limit_in_mb = 13;
      Specified by:
      getGpuHostMemLimitInMb in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The gpuHostMemLimitInMb.

    • getGpuHostMemDisallowGrowth

      public boolean getGpuHostMemDisallowGrowth()
       If true, then the host allocator allocates its max memory all upfront and
 never grows.  This can be useful for latency-sensitive systems, because
 growing the GPU host memory pool can be expensive.

 You probably only want to use this in combination with
 gpu_host_mem_limit_in_mb, because the default GPU host memory limit is
 quite high.
      bool gpu_host_mem_disallow_growth = 14;
      Specified by:
      getGpuHostMemDisallowGrowth in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The gpuHostMemDisallowGrowth.

    • getGpuSystemMemorySizeInMb

      public int getGpuSystemMemorySizeInMb()
       Memory limit for gpu system. This can also be set by
 TF_DEVICE_MIN_SYS_MEMORY_IN_MB, which takes precedence over
 gpu_system_memory_size_in_mb. With this, user can configure the gpu
 system memory size for better resource estimation of multi-tenancy(one
 gpu with multiple model) use case.
      int32 gpu_system_memory_size_in_mb = 16;
      Specified by:
      getGpuSystemMemorySizeInMb in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The gpuSystemMemorySizeInMb.

    • getPopulatePjrtGpuClientCreationInfo

      public boolean getPopulatePjrtGpuClientCreationInfo()
       If true, save information needed for created a PjRt GPU client for
 creating a client with remote devices.
      bool populate_pjrt_gpu_client_creation_info = 17;
      Specified by:
      getPopulatePjrtGpuClientCreationInfo in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The populatePjrtGpuClientCreationInfo.

    • getNodeId

      public int getNodeId()
       node_id for use when creating a PjRt GPU client with remote devices,
 which enumerates jobs*tasks from a ServerDef.
      int32 node_id = 18;
      Specified by:
      getNodeId in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The nodeId.

    • hasStreamMergeOptions

      public boolean hasStreamMergeOptions()
      .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
      Specified by:
      hasStreamMergeOptions in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      Whether the streamMergeOptions field is set.

    • getStreamMergeOptions

      public GPUOptions.Experimental.StreamMergeOptions getStreamMergeOptions()
      .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
      Specified by:
      getStreamMergeOptions in interface GPUOptions.ExperimentalOrBuilder
      Returns:
      The streamMergeOptions.

    • getStreamMergeOptionsOrBuilder

      public GPUOptions.Experimental.StreamMergeOptionsOrBuilder getStreamMergeOptionsOrBuilder()
      .tensorflow.GPUOptions.Experimental.StreamMergeOptions stream_merge_options = 19;
      Specified by:
      getStreamMergeOptionsOrBuilder in interface GPUOptions.ExperimentalOrBuilder

    • isInitialized

      public final boolean isInitialized()
      Specified by:
      isInitialized in interface com.google.protobuf.MessageLiteOrBuilder
      Overrides:
      isInitialized in class com.google.protobuf.GeneratedMessageV3

    • writeTo

      public void writeTo(com.google.protobuf.CodedOutputStream output) throws IOException
      Specified by:
      writeTo in interface com.google.protobuf.MessageLite
      Overrides:
      writeTo in class com.google.protobuf.GeneratedMessageV3
      Throws:
      IOException

    • getSerializedSize

      public int getSerializedSize()
      Specified by:
      getSerializedSize in interface com.google.protobuf.MessageLite
      Overrides:
      getSerializedSize in class com.google.protobuf.GeneratedMessageV3

    • equals

      public boolean equals(Object obj)
      Specified by:
      equals in interface com.google.protobuf.Message
      Overrides:
      equals in class com.google.protobuf.AbstractMessage

    • hashCode

      public int hashCode()
      Specified by:
      hashCode in interface com.google.protobuf.Message
      Overrides:
      hashCode in class com.google.protobuf.AbstractMessage

    • parseFrom

      public static GPUOptions.Experimental parseFrom(ByteBuffer data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(ByteBuffer data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(com.google.protobuf.ByteString data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(com.google.protobuf.ByteString data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(byte[] data) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(byte[] data, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws com.google.protobuf.InvalidProtocolBufferException
      Throws:
      com.google.protobuf.InvalidProtocolBufferException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(InputStream input) throws IOException
      Throws:
      IOException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • parseDelimitedFrom

      public static GPUOptions.Experimental parseDelimitedFrom(InputStream input) throws IOException
      Throws:
      IOException

    • parseDelimitedFrom

      public static GPUOptions.Experimental parseDelimitedFrom(InputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(com.google.protobuf.CodedInputStream input) throws IOException
      Throws:
      IOException

    • parseFrom

      public static GPUOptions.Experimental parseFrom(com.google.protobuf.CodedInputStream input, com.google.protobuf.ExtensionRegistryLite extensionRegistry) throws IOException
      Throws:
      IOException

    • newBuilderForType

      public GPUOptions.Experimental.Builder newBuilderForType()
      Specified by:
      newBuilderForType in interface com.google.protobuf.Message
      Specified by:
      newBuilderForType in interface com.google.protobuf.MessageLite

    • newBuilder

      public static GPUOptions.Experimental.Builder newBuilder()

    • newBuilder

      public static GPUOptions.Experimental.Builder newBuilder(GPUOptions.Experimental prototype)

    • toBuilder

      public GPUOptions.Experimental.Builder toBuilder()
      Specified by:
      toBuilder in interface com.google.protobuf.Message
      Specified by:
      toBuilder in interface com.google.protobuf.MessageLite

    • newBuilderForType

      protected GPUOptions.Experimental.Builder newBuilderForType(com.google.protobuf.GeneratedMessageV3.BuilderParent parent)
      Specified by:
      newBuilderForType in class com.google.protobuf.GeneratedMessageV3

    • getDefaultInstance

      public static GPUOptions.Experimental getDefaultInstance()

    • parser

      public static com.google.protobuf.Parser<GPUOptions.Experimental> parser()

    • getParserForType

      public com.google.protobuf.Parser<GPUOptions.Experimental> getParserForType()
      Specified by:
      getParserForType in interface com.google.protobuf.Message
      Specified by:
      getParserForType in interface com.google.protobuf.MessageLite
      Overrides:
      getParserForType in class com.google.protobuf.GeneratedMessageV3

    • getDefaultInstanceForType

      public GPUOptions.Experimental getDefaultInstanceForType()
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageLiteOrBuilder
      Specified by:
      getDefaultInstanceForType in interface com.google.protobuf.MessageOrBuilder