공개 최종 클래스 GPUOptions
Protobuf 유형 tensorflow.GPUOptions
중첩 클래스
| 수업 | GPU옵션.빌더 | Protobuf 유형 tensorflow.GPUOptions | |
| 수업 | GPUOptions.Experimental | Protobuf 유형 tensorflow.GPUOptions.Experimental | |
| 상호 작용 | GPUOptions.ExperimentalOrBuilder | ||
상수
공개 방법
| 부울 | 같음 (객체 객체) |
| 끈 | getAllocatorType () The type of GPU allocation strategy to use. |
| com.google.protobuf.ByteString | getAllocatorTypeBytes () The type of GPU allocation strategy to use. |
| 부울 | getAllowGrowth () If true, the allocator does not pre-allocate the entire specified GPU memory region, instead starting small and growing as needed. |
| 정적 GPU옵션 | |
| GPU옵션 | |
| 긴 | getDeferredDeletionBytes () Delay deletion of up to this many bytes to reduce the number of interactions with gpu driver code. |
| 최종 정적 com.google.protobuf.Descriptors.Descriptor | |
| 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. |
| GPUOptions.ExperimentalOrBuilder | getExperimentalOrBuilder () Everything inside experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| 부울 | getForceGpu호환 () Force all tensors to be gpu_compatible. |
| 더블 | getPerProcessGpuMemoryFraction () Fraction of the available GPU memory to allocate for each process. |
| 정수 | getPollingActiveDelayUsecs () In the event polling loop sleep this many microseconds between PollEvents calls, when the queue is not empty. |
| 정수 | getPollingInactiveDelayMsecs () This field is deprecated and ignored. |
| 정수 | |
| 최종 com.google.protobuf.UnknownFieldSet | |
| 끈 | getVisibleDeviceList () A comma-separated list of GPU ids that determines the 'visible' to 'virtual' mapping of GPU devices. |
| com.google.protobuf.ByteString | getVisibleDeviceListBytes () A comma-separated list of GPU ids that determines the 'visible' to 'virtual' mapping of GPU devices. |
| 부울 | has실험적 () Everything inside experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat. |
| 정수 | 해시 코드 () |
| 최종 부울 | 초기화됨 () |
| 정적 GPUOptions.Builder | newBuilder ( GPUOptions 프로토타입) |
| 정적 GPUOptions.Builder | 새로운 빌더 () |
| GPU옵션.빌더 | |
| 정적 GPU옵션 | parsDelimitedFrom (InputStream 입력) |
| 정적 GPU옵션 | parseDelimitedFrom (InputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| 정적 GPU옵션 | parsFrom (ByteBuffer 데이터) |
| 정적 GPU옵션 | ParseFrom (com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| 정적 GPU옵션 | ParseFrom (ByteBuffer 데이터, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| 정적 GPU옵션 | ParseFrom (com.google.protobuf.CodedInputStream 입력) |
| 정적 GPU옵션 | parseFrom (byte[] 데이터, com.google.protobuf.ExtensionRegistryLite 확장Registry) |
| 정적 GPU옵션 | ParseFrom (com.google.protobuf.ByteString 데이터) |
| 정적 GPU옵션 | ParseFrom (InputStream 입력, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| 정적 GPU옵션 | ParseFrom (com.google.protobuf.ByteString 데이터, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry) |
| 공전 | 파서 () |
| GPU옵션.빌더 | toBuilder () |
| 무효의 | writeTo (com.google.protobuf.CodedOutputStream 출력) |
상속된 메서드
상수
공개 정적 최종 int ALLOCATOR_TYPE_FIELD_NUMBER
상수값: 2
공개 정적 최종 int ALLOW_GROWTH_FIELD_NUMBER
상수값: 4
공개 정적 최종 int DEFERRED_DELETION_BYTES_FIELD_NUMBER
상수값: 3
공개 정적 최종 int EXPERIMENTAL_FIELD_NUMBER
상수값: 9
공개 정적 최종 int FORCE_GPU_COMPATIBLE_FIELD_NUMBER
상수값: 8
공개 정적 최종 int PER_PROCESS_GPU_MEMORY_FRACTION_FIELD_NUMBER
상수값: 1
공개 정적 최종 int POLLING_ACTIVE_DELAY_USECS_FIELD_NUMBER
상수값: 6
공개 정적 최종 int POLLING_INACTIVE_DELAY_MSECS_FIELD_NUMBER
상수값: 7
공개 정적 최종 int VISIBLE_DEVICE_LIST_FIELD_NUMBER
상수값: 5
공개 방법
공개 부울은 (객체 obj)와 같습니다 .
공개 문자열 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; 공개 com.google.protobuf.ByteString getAllocatorTypeBytes ()
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; 공개 부울 getAllowGrowth ()
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; 공개 긴 getDeferredDeletionBytes ()
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; 공개 정적 최종 com.google.protobuf.Descriptors.Descriptor getDescriptor ()
공개 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; 공개 GPUOptions.ExperimentalOrBuilder getExperimentalOrBuilder ()
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; 공개 부울 getForceGpuCompatible ()
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; 공공의
공개 이중 getPerProcessGpuMemoryFraction ()
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; 공개 int getPollingActiveDelayUsecs ()
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; 공개 int getPollingInactiveDelayMsecs ()
This field is deprecated and ignored.
int32 polling_inactive_delay_msecs = 7; 공개 int getSerializedSize ()
공개 최종 com.google.protobuf.UnknownFieldSet getUnknownFields ()
공개 문자열 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 "platform 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; 공개 com.google.protobuf.ByteString getVisibleDeviceListBytes ()
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 "platform 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; 공개 부울 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; 공개 int hashCode ()
공개 최종 부울 isInitialized ()
공개 정적 GPUOptionsparseDelimitedFrom ( InputStream 입력, com.google.protobuf.ExtensionRegistryLite 확장Registry)
던지기
| IO예외 |
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공개 정적 GPUOptionsparseFrom ( com.google.protobuf.CodedInputStream 입력, com.google.protobuf.ExtensionRegistryLite 확장Registry)
던지기
| IO예외 |
|---|
공개 정적 GPUOptions parsFrom (ByteBuffer 데이터, com.google.protobuf.ExtensionRegistryLite ExtensionRegistry)
던지기
| 잘못된프로토콜버퍼예외 |
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공개 정적 GPUOptions 구문 분석 (byte[] 데이터, com.google.protobuf.ExtensionRegistryLite 확장Registry)
던지기
| 잘못된프로토콜버퍼예외 |
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공개 정적 GPUOptionsparseFrom ( InputStream 입력, com.google.protobuf.ExtensionRegistryLite 확장Registry)
던지기
| IO예외 |
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공개 정적 GPUOptionsparseFrom ( com.google.protobuf.ByteString 데이터, com.google.protobuf.ExtensionRegistryLite 확장Registry)
던지기
| 잘못된프로토콜버퍼예외 |
|---|
공개 정적
공개 무효 writeTo (com.google.protobuf.CodedOutputStream 출력)
던지기
| IO예외 |
|---|