NVIDIA NvNeural SDK
2022.2
GPU inference framework for NVIDIA Nsight Deep Learning Designer
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Structure describing details of an object's memory allocation behavior. More...
#include <nvneural/CoreTypes.h>
Public Attributes | |
std::size_t | peakCommittedMemory |
This is the high water mark of on device memory (committed memory). | |
std::size_t | peakRequestedMemory |
This is the high water mark of requested memory. More... | |
std::size_t | persistingCommittedMemory |
This is the high water mark of on device memory (committed memory) that persists after a network is launched. More... | |
std::size_t | persistingRequestedMemory |
This is the high water mark of requested memory that persists after a network finishes an inference run. More... | |
std::size_t | structSize |
This is the sizeof() this struct. More... | |
Structure describing details of an object's memory allocation behavior.
Use INetworkBackend2::getMemoryTrackingData to obtain instances of this structure.
std::size_t nvneural::MemoryTrackingData::peakRequestedMemory |
This is the high water mark of requested memory.
Requested memory can differ from committed memory in that on device memory may be in preallocated buffers.
std::size_t nvneural::MemoryTrackingData::persistingCommittedMemory |
This is the high water mark of on device memory (committed memory) that persists after a network is launched.
Usually weight data or permanent layers.
std::size_t nvneural::MemoryTrackingData::persistingRequestedMemory |
This is the high water mark of requested memory that persists after a network finishes an inference run.
Usually weight data or permanent layers. Requested memory can differ from committed memory in that on device memory may be in preallocated buffers.
std::size_t nvneural::MemoryTrackingData::structSize |
This is the sizeof() this struct.
It is used for versioning. Future versions of MemoryTrackingData will be layout-compatible with this structure. Use structSize to check for v2, v3, etc., and then cast to the newer structure if appropriate.