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* samples/python/benchmark/bert_benhcmark/requirements.txt: add datasets, install torch cpu only for linux By default for linux torch is installed with GPU support, but for windows is CPU only * benhcmark->benchmark
Bert Benchmark Python* Sample
This sample demonstrates how to estimate performace of a Bert model using Asynchronous Inference Request API. Unlike [demos](@ref omz_demos) this sample doesn't have configurable command line arguments. Feel free to modify sample's source code to try out different options.
The following Python* API is used in the application:
| Feature | API | Description |
|---|---|---|
| OpenVINO Runtime Version | [openvino.runtime.get_version] | Get Openvino API version |
| Basic Infer Flow | [openvino.runtime.Core], [openvino.runtime.Core.compile_model] | Common API to do inference: compile a model |
| Asynchronous Infer | [openvino.runtime.AsyncInferQueue], [openvino.runtime.AsyncInferQueue.start_async], [openvino.runtime.AsyncInferQueue.wait_all] | Do asynchronous inference |
| Model Operations | [openvino.runtime.CompiledModel.inputs] | Get inputs of a model |
How It Works
The sample downloads a model and a tokenizer, export the model to onnx, reads the exported model and reshapes it to enforce dynamic inpus shapes, compiles the resulting model, downloads a dataset and runs benchmarking on the dataset.
You can see the explicit description of each sample step at Integration Steps section of "Integrate OpenVINO™ Runtime with Your Application" guide.
Running
Install the openvino Python package:
python -m pip install openvino
Install packages from requirements.txt:
python -m pip install -r requirements.txt
Run the sample
python bert_benchmark.py
Sample Output
The sample outputs how long it takes to process a dataset.
See Also
- Integrate the OpenVINO™ Runtime with Your Application
- Using OpenVINO™ Toolkit Samples
- [Model Downloader](@ref omz_tools_downloader)
- Model Optimizer