a19c34a0f7
### Details:
Numpy started preparation to release 2.0 version.
It brings many breaking changes listed below.
Align with these changes it's a plan for further development
(CVS-138766), for today we want to secure users against unintentional
installation of 2.x version of numpy with OV.
This PR has to be ported for 2024/1 branch
- Preparing to restrict OV to not use release of Numpy 2.0 which may
introduce many breaking changes including:
- required bump of `pybind` to 2.12+
- API and namespaces clean-ups and deprecations
- behavior changes of keywords such as `copy`
- changes to existing and introduction of new dtypes (such as
`StringDType` and possibility of adding custom ones)
- more can be found here:
https://numpy.org/devdocs/release/2.0.0-notes.html
- and here:
https://numpy.org/devdocs/numpy_2_0_migration_guide.html#numpy-2-migration-guide
### Tickets:
- CVS-138838
---------
Co-authored-by: Michal Lukaszewski <michal.lukaszewski@intel.com>
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| openvino | ||
| CMakeLists.txt | ||
| README.md | ||
| benchmark_app.py | ||
| requirements.txt | ||
| setup.py | ||
README.md
Benchmark Python Tool
This page demonstrates how to use the Benchmark Python Tool to estimate deep learning inference performance on supported devices.
NOTE: This page describes usage of the Python implementation of the Benchmark Tool. For the C++ implementation, refer to the Benchmark C++ Tool page. The Python version is recommended for benchmarking models that will be used in Python applications, and the C++ version is recommended for benchmarking models that will be used in C++ applications. Both tools have a similar command interface and backend.
For more detailed information on how this sample works, check the dedicated article
Requriements
The Python benchmark_app is automatically installed when you install OpenVINO Developer Tools using PyPI Before running benchmark_app, make sure the openvino_env virtual environment is activated, and navigate to the directory where your model is located.
The benchmarking application works with models in the OpenVINO IR (model.xml and model.bin) and ONNX (model.onnx) formats.
Make sure to convert your models if necessary.