* add placeholder for python version of first snippet
* fix problem with placeholder
* fix wrong file name
* fix fragment name
* update python snippets
* move imports to the top of the code fragments
* auto-batching- bare min of the info
* renaming BATCH.MD to the automatic_batching.md, also aligned the link to the new naming convention
* more info and brushed
* added openvino_docs_OV_UG_Automatic_Batching to the main TOC
* Apply suggestions from code review
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
* close on the comments, added the code examples
* Apply suggestions from code review
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
* Update example
* Update format
* Update docs format
* added couple of more perf considerations
* more code examples
* Apply suggestions from code review
* Apply the rest from code review
* Update header
Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
* Fixed some comments about transformations
* Changed transformation guide
* Fixed typo
* Moved transformation doc to extensibility
* Moved images to Extensibility_UG
* Added separate document for each pass
* Added see also section
* Fixed comments
fix canConvolutionBeTransformed arguments
fix isAsymmetricOnWeights in GPU plugin
added defaultPrecisions in TestTransformationParams
set new default attribute precisions
try to set const default precisions in network_helper.cpp
apply precision_set
[LPT] Default precisions
rebase
remove extra const
used defaultPrecision in tests
fixed SimpleLowPrecisionTransformer default argument
fixed AttributeParameters default argument
added defaultPrecisions in functions
fix assign_and_read_value_transformation tests
fixed wrong defaultPrecisions definition
fixed ConcatWithNeighborsWithConvolutionTransformation tests
remove getDefaultPrecisions
rebase
remove getDefaultPrecisions from gpu plugin
remove getDefaultPrecisions from lpt_mkldnn_plugin.cpp
use predefined member
update mkldnn_plugin.cpp & lpt_mkldnn_plugin.cpp
resolved conversations
make all lambda captures by ref
* Used new config for streams and threads
* Fixed review coments in ba
* format fix
* fixed hello_query_device
* Added STL string io
* fixed tests
* Fixed test
* Fixed build
* fixed format
* Fixed build
* try fix win
* other any io specialization
* Fixed after merge
* renamed streams
* build fixed
* fixed build
* fixed format
* fix for old mac build
* Fixed type of exception
* test fix
* [LPT] Documentation
* 1) ToC was removed 2) SVG => PNG temporary conversion
* [LPT] Refactoring + developer guide
* [LPT] attribute doxygen documentation was added
* [LPT] Developer Guide to Reference API links were added
* [LPT] comments fixes
* [LPT] Reference API to Developer Guide links were added
* [LPT] titles were changed
* [LPT] comments fixes#2
* [LPT] root document was moved to Plugin DG
* [LPT] Documentation: image link quick fix
* [LPT] Docummentation: PrecisionsAttribute description quick fix
* fix comments from Karol
* fixes
* movement
* directive was added
* movement #2
* LPT reference in Executable Network rollback
* snippets were updated ini accordance with new API
* auto-batching POC squashed (all commits from auto-batch-2021.3 branch)
(cherry picked from commit d7742f2c747bc514a126cc9a4d5b99f0ff5cbbc7)
* applying/accomodating the API changes after rebase to the master
* replaying modified version of actual batch selection
* eearly experiments with model mem footprint
* changes from rebasing to the latest master
* experimenting with DG1 on the batch size selection, also collecting the mem footprint
* WIP:moving the auto-batching to the icore to let the MULT/AUTO support that, ALLOW_AUTO_BATCHING as a conventional config key. still fials hot device swap
* quick-n-dirty batch footpint vs device total mem
* code style
* testing which models perform badly due to kernels and NOT (batched) footprint
* stub pipeline task to comunicate the readiness rather than promise/future
* quick-n-dirty timeout impl
* explicit _completionTasks,reverting BA to use the timeout
* inputs outputs copies, works with AUTO and demo now
* accomodate the config per device-id, after rebase to the latest master
* allowing the auto-batching only with tput hint to let more conventional tests pass
* fix the pre-mature timeout restaring via waiting for batch1 requests completion
* moved the bacthed request statring ( along with input copies) to the dedicated thread
* [IE CLDNN] Disable bs_fs_yx_bsv16_fsv16 format for int8 convolution
* code style
* increasing the timeout to test the ssd_* models perf (timeout?) issues
* reducing number of output stuff in BA to avoid bloating the logs in experiments
* more aggressive batching for experiments, not limited to 32 and also 4 as a min
* more accurate timeout debugging info
* getting the reqs limitation from the plugin SetConfig as well
* refactor the reshape logic a bit to accomodate CPU for bathcing, also added remeote context
* let the benchamrk_app to consume specific batch values for the auto-batching such as BATCH:GPU(4)
* auto-batching functional test (with results check vs ref) and GPU instance for that
* fixed arithemtic on blobs ptrs
* clang
* handling possible batched network failure
* BATCH as the constants device name in test
* ENABLE_BATCH
* func tests for CPU, also DetectionOutput hetero tests (CPU and GPU)
* DetectionOutput hetero test for the CPU
* reenabling the Auto-Batching in the AUTO
* auto-batching device enabled in the test
* fixed the DO test
* improve the loading loop logic
* brushed the config keys
* allow hetero code-path for explicit device name like BATCH:GPU(4), used in the hetero code-path tests
* fix the test after refactoring
* clang
* moving ThreadSafeQueue to the ie_parallel, as it is re-used in the AUTO/MULTI and BATCH now
* auto-batching hetero test (subgraph with DetectionOutput)
* fixed minor changes that were result of experiments with impl
* code-style
* brushing, disabling CPU's HETERO tests until planned activity for 22.2
* removing home-baked MAX_BATCH_SZIE and swicthing to the official impl by GPU team
* remote blobs tests for the auto-batching (old API)
* brushed names a bit
* CreateContext and LoadNEtwork with context for the Auto-Batching plus remote-blobs tests
* fixed the ieUnitTests with adding CreateContext stub to the MockICore
* clang
* improved remote-blobs tests
* revert the back BA from exeprimenents with AB + device_use_mem
* conformance tests for BATCH, alos batch size 1 is default for BATCH:DEVICE
* remote blobs 2.0 tests, issue with context having the orig device name
* debugging DG1 perf drop (presumably due to non-fitting the device-mem)
* disbaling WA with batch/=2 for excesive mem footptint, leaving only streams 2
* remote blobs 2.0 tests for different tensor sharing types
* converting assert to throw to accomodate legacy API where the lock() was possible to be called
* revert the timeout back to avoid mixing the studies, fixed the footprint calc
* reverting to estimating the max batch by extrapolating from bacth1 size
* more conservative footptint etimation (with bacth1), graceful bacth 1 handling without duplication
* even graceful batch 1 handling without duplication
* WA for MAX_BATCH_SIZE failure, removing batch4 as a min for the auto-batching
* AutoBatchPlugin -> ov_auto_batch_plugin
* WA for gcc 4.8
* clang
* fix misprint
* fixed errors resulted from recent OV's Variant to Any transition
* skip auto-batching for already-batched networks
* AUTO_BATCH_TIMEOUT and tests
* GPU-specific L3
* switched to pure config, also improved ALLOW_AUTO_BATCHING config key handling logic
* debugging device info
* enabling the config tests for the GPU and fixing the Auto-batching tests to pass
* making the default (when not recognized the driver) cache size more aggressive, to accomodate recent HW with old drivers
* skip auto-batching for RNNs and alikes (e.g. single CHW input)
* fixed fallback to the bacth1 and moved HETERO path under condition to avoid bloating
* brushing
* Auto plugin GetMetric support gpu auto-batch
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* add test case
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* add comments on test
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* brushing the vars names, alos adding the excpetion handling
* disabling the auto-batching for the networks with non-batched outputs and faster-rcnn and alikes (CVS-74085) to minimize the of #failures
* add try catch
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* brushing the code changed in the GPU plugin
* Auto-Batch requests tests
* brushed varibles a bit (ref)
* cleaned debug output from the ie_core
* cleaned cmake for the Auto-Batch
* removed batchN estimation from batch1
* cleaned from debug printf
* comments, cleanup
* WA the mock test errors introduced with merging the https://github.com/myshevts/openvino/pull/13
* Adding back removed batchN estimation from batch1 to debug degradations on DG1 (resulted from too optimistic MAX_BATCH_SIZE?). This partially reverts commit e8f1738ac1.
* brushing ie_core.cpp
* fix 32bit compilation
* Code review: ENABLE_AUTO_BATCH
* consolidate the auot-batching logic in ie_core.cpp into single ApplyAutoBAtching
* renamed brushed the OPTIMAL_BATCH (now with_SIZE) and mimicks the MAX_BATCH_SZIE wrt MODEL_PTR
* default value for the OPTIMAL_BATCH_SIZE
* clang
* accomodate new func tests location
* fix shuffle of headers after clang + copyrights
* fixed misprint made during code refactoring
* moving the common therad-safe containers (like ThreadSafeQueue) to the dedicated dev_api header
* switch from the device name to the OPTIMAL_BATCH_SIZE metric presence as a conditin to consider Auto-Batching
* switching from the unsafe size() and minimizing time under lock
* code style
* brushed the ApplyAutoBatching
* brushed the netric/config names and descriptions
* completed the core intergration tests for the auto-batching
* ExecGraphInfo and check for incorrect cfg
* removed explicit dependencies from cmake file of the plugin
* disabling Auto-Batching thru the tput hint (to preserve current product default), only excplicit like BATCH:GPU used in the tests
Co-authored-by: Roman Lyamin <roman.lyamin@intel.com>
Co-authored-by: Hu, Yuan2 <yuan2.hu@intel.com>
* Remove some legacy targets
* Replace some targets
* Removed inference_engine_plugin_api dependency
* Minor comment for developer config
* Fixed include paths
* Small fixes for static build
* Try to fix build pyopenvino
* Fixed comments
* Try to fix build
* Include OpenVINODeveloperPackage inside InferenceEngineDeveloperPackageConfig
* Try to fix GAPI tests
* Renamed ov::Function to ov::Model
* Fixed all for macos
* Fixed build
* Fixed build
* Revert changes in GPU plugin
* Fixed ngraphFunctions
* Fixed all for mac
* Fixed new test
* Fixed if for Windows
* Fixed unit tests and renamed Function in python API
* Fixed code style
* Fixed import
* Fixed conflict
* Fixed merge issues
* Renaming all frontends from "*_ngraph_frontend*" to "_ov_frontend*"
Also Debug builds on Windows release frontends without "d" suffix will not be loaded
* Fix review comments and add wheels test debug prints
* More debug prints
* Load by absolute path and remove debug prints
* replace AUTO_DEVICE_LIST with MULTI_DEVICE_PRIORITY in test case
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* remove KEY_AUTO_DEVICE_LIST
Signed-off-by: Hu, Yuan2 <yuan2.hu@intel.com>
* Merge onnx_importer to onnx_ngraph_frontend
* address review comments
* use onnx_ngraph_frontend in docs/template_extension
* link onnx_ngraph_frontend instead of onnx_importer
* switch onnxruntime branch to rel-1.8.1_ov_2022.1 in https://github.com/intel/onnxruntime
* Remove pip install cmake since the 1.8.1 release doesnt require it
* add alias
* set IMPORTED_GLOBAL
Co-authored-by: Ilya Lavrenov <ilya.lavrenov@intel.com>
