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Java API for TensorFlow protocol buffers.
There is a newer version: 1.15.0
// Generated by the protocol buffer compiler.  DO NOT EDIT!
// source: tensorflow/core/protobuf/config.proto

package org.tensorflow.framework;

public interface GPUOptionsOrBuilder extends
    // @@protoc_insertion_point(interface_extends:tensorflow.GPUOptions)
    com.google.protobuf.MessageOrBuilder {

  /**
   *    * A value between 0 and 1 that indicates what fraction of the
   * available GPU memory to pre-allocate for each process.  1 means
   * to pre-allocate all of the GPU memory, 0.5 means the process
   * allocates ~50% of the available GPU memory.
   * 
   *
   * double per_process_gpu_memory_fraction = 1;
   */
  double getPerProcessGpuMemoryFraction();

  /**
   *    * The type of GPU allocation strategy to use.
   * Allowed values:
   * "": The empty string (default) uses a system-chosen default
   *     which may change over time.
   * "BFC": A "Best-fit with coalescing" algorithm, simplified from a
   *        version of dlmalloc.
   * 
   *
   * string allocator_type = 2;
   */
  java.lang.String getAllocatorType();
  /**
   *    * The type of GPU allocation strategy to use.
   * Allowed values:
   * "": The empty string (default) uses a system-chosen default
   *     which may change over time.
   * "BFC": A "Best-fit with coalescing" algorithm, simplified from a
   *        version of dlmalloc.
   * 
   *
   * string allocator_type = 2;
   */
  com.google.protobuf.ByteString
      getAllocatorTypeBytes();

  /**
   *    * Delay deletion of up to this many bytes to reduce the number of
   * interactions with gpu driver code.  If 0, the system chooses
   * a reasonable default (several MBs).
   * 
   *
   * int64 deferred_deletion_bytes = 3;
   */
  long getDeferredDeletionBytes();

  /**
   *    * If true, the allocator does not pre-allocate the entire specified
   * GPU memory region, instead starting small and growing as needed.
   * 
   *
   * bool allow_growth = 4;
   */
  boolean getAllowGrowth();

  /**
   *    * A comma-separated list of GPU ids that determines the 'visible'
   * to 'virtual' mapping of GPU devices.  For example, if TensorFlow
   * can see 8 GPU devices in the process, and one wanted to map
   * visible GPU devices 5 and 3 as "/gpu:0", and "/gpu:1", then one
   * would specify this field as "5,3".  This field is similar in
   * spirit to the CUDA_VISIBLE_DEVICES environment variable, except
   * it applies to the visible GPU devices in the process.
   * NOTE: The GPU driver provides the process with the visible GPUs
   * in an order which is not guaranteed to have any correlation to
   * the *physical* GPU id in the machine.  This field is used for
   * remapping "visible" to "virtual", which means this operates only
   * after the process starts.  Users are required to use vendor
   * specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
   * physical to visible device mapping prior to invoking TensorFlow.
   * 
   *
   * string visible_device_list = 5;
   */
  java.lang.String getVisibleDeviceList();
  /**
   *    * A comma-separated list of GPU ids that determines the 'visible'
   * to 'virtual' mapping of GPU devices.  For example, if TensorFlow
   * can see 8 GPU devices in the process, and one wanted to map
   * visible GPU devices 5 and 3 as "/gpu:0", and "/gpu:1", then one
   * would specify this field as "5,3".  This field is similar in
   * spirit to the CUDA_VISIBLE_DEVICES environment variable, except
   * it applies to the visible GPU devices in the process.
   * NOTE: The GPU driver provides the process with the visible GPUs
   * in an order which is not guaranteed to have any correlation to
   * the *physical* GPU id in the machine.  This field is used for
   * remapping "visible" to "virtual", which means this operates only
   * after the process starts.  Users are required to use vendor
   * specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
   * physical to visible device mapping prior to invoking TensorFlow.
   * 
   *
   * string visible_device_list = 5;
   */
  com.google.protobuf.ByteString
      getVisibleDeviceListBytes();

  /**
   *    * In the event polling loop sleep this many microseconds between
   * PollEvents calls, when the queue is not empty.  If value is not
   * set or set to 0, gets set to a non-zero default.
   * 
   *
   * int32 polling_active_delay_usecs = 6;
   */
  int getPollingActiveDelayUsecs();

  /**
   *    * In the event polling loop sleep this many millisconds between
   * PollEvents calls, when the queue is empty.  If value is not
   * set or set to 0, gets set to a non-zero default.
   * 
   *
   * int32 polling_inactive_delay_msecs = 7;
   */
  int getPollingInactiveDelayMsecs();

  /**
   *    * Force all tensors to be gpu_compatible. On a GPU-enabled TensorFlow,
   * enabling this option forces all CPU tensors to be allocated with Cuda
   * pinned memory. Normally, TensorFlow will infer which tensors should be
   * allocated as the pinned memory. But in case where the inference is
   * incomplete, this option can significantly speed up the cross-device memory
   * copy performance as long as it fits the memory.
   * Note that this option is not something that should be
   * enabled by default for unknown or very large models, since all Cuda pinned
   * memory is unpageable, having too much pinned memory might negatively impact
   * the overall host system performance.
   * 
   *
   * bool force_gpu_compatible = 8;
   */
  boolean getForceGpuCompatible();
}