public interface GPUOptionsOrBuilder
extends com.google.protobuf.MessageOrBuilder
Modifier and Type | Method and Description |
---|---|
String |
getAllocatorType()
The type of GPU allocation strategy to use.
|
com.google.protobuf.ByteString |
getAllocatorTypeBytes()
The type of GPU allocation strategy to use.
|
boolean |
getAllowGrowth()
If true, the allocator does not pre-allocate the entire specified
GPU memory region, instead starting small and growing as needed.
|
long |
getDeferredDeletionBytes()
Delay deletion of up to this many bytes to reduce the number of
interactions with gpu driver code.
|
boolean |
getForceGpuCompatible()
Force all tensors to be gpu_compatible.
|
double |
getPerProcessGpuMemoryFraction()
A value between 0 and 1 that indicates what fraction of the
available GPU memory to pre-allocate for each process.
|
int |
getPollingActiveDelayUsecs()
In the event polling loop sleep this many microseconds between
PollEvents calls, when the queue is not empty.
|
int |
getPollingInactiveDelayMsecs()
In the event polling loop sleep this many millisconds between
PollEvents calls, when the queue is empty.
|
String |
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.
|
findInitializationErrors, getAllFields, getDefaultInstanceForType, getDescriptorForType, getField, getInitializationErrorString, getOneofFieldDescriptor, getRepeatedField, getRepeatedFieldCount, getUnknownFields, hasField, hasOneof
double getPerProcessGpuMemoryFraction()
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;
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()
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;
long 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;
boolean 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;
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()
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;
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;
int getPollingInactiveDelayMsecs()
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;
boolean 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;
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