org.tensorflow.framework.GPUOptionsOrBuilder Maven / Gradle / Ivy
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// 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)
org.nd4j.shade.protobuf.MessageOrBuilder {
/**
*
* Fraction of the available GPU memory to allocate for each process.
* 1 means to allocate all of the GPU memory, 0.5 means the process
* allocates up to ~50% of the available GPU memory.
* GPU memory is pre-allocated unless the allow_growth option is enabled.
* If greater than 1.0, uses CUDA unified memory to potentially oversubscribe
* the amount of memory available on the GPU device by using host memory as a
* swap space. Accessing memory not available on the device will be
* significantly slower as that would require memory transfer between the host
* and the device. Options to reduce the memory requirement should be
* considered before enabling this option as this may come with a negative
* performance impact. Oversubscription using the unified memory requires
* Pascal class or newer GPUs and it is currently only supported on the Linux
* operating system. See
* https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-requirements
* for the detailed requirements.
*
*
* double per_process_gpu_memory_fraction = 1;
* @return The perProcessGpuMemoryFraction.
*/
double getPerProcessGpuMemoryFraction();
/**
*
* 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;
* @return The allowGrowth.
*/
boolean getAllowGrowth();
/**
*
* 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;
* @return The allocatorType.
*/
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;
* @return The bytes for allocatorType.
*/
org.nd4j.shade.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;
* @return The deferredDeletionBytes.
*/
long getDeferredDeletionBytes();
/**
*
* 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 "/device:GPU:0", and "/device: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:
* 1. 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.
* 2. In the code, the ids in this list are also called "CUDA GPU id"s,
* and the 'virtual' ids of GPU devices (i.e. the ids in the device
* name "/device:GPU:<id>") are also called "TF GPU id"s. Please
* refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h
* for more information.
*
*
* string visible_device_list = 5;
* @return The visibleDeviceList.
*/
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 "/device:GPU:0", and "/device: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:
* 1. 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.
* 2. In the code, the ids in this list are also called "CUDA GPU id"s,
* and the 'virtual' ids of GPU devices (i.e. the ids in the device
* name "/device:GPU:<id>") are also called "TF GPU id"s. Please
* refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h
* for more information.
*
*
* string visible_device_list = 5;
* @return The bytes for visibleDeviceList.
*/
org.nd4j.shade.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;
* @return The pollingActiveDelayUsecs.
*/
int getPollingActiveDelayUsecs();
/**
*
* This field is deprecated and ignored.
*
*
* int32 polling_inactive_delay_msecs = 7;
* @return The pollingInactiveDelayMsecs.
*/
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;
* @return The forceGpuCompatible.
*/
boolean getForceGpuCompatible();
/**
*
* Everything inside experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* .tensorflow.GPUOptions.Experimental experimental = 9;
* @return Whether the experimental field is set.
*/
boolean hasExperimental();
/**
*
* Everything inside experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* .tensorflow.GPUOptions.Experimental experimental = 9;
* @return The experimental.
*/
org.tensorflow.framework.GPUOptions.Experimental getExperimental();
/**
*
* Everything inside experimental is subject to change and is not subject
* to API stability guarantees in
* https://www.tensorflow.org/guide/version_compat.
*
*
* .tensorflow.GPUOptions.Experimental experimental = 9;
*/
org.tensorflow.framework.GPUOptions.ExperimentalOrBuilder getExperimentalOrBuilder();
}