44 lines
2.4 KiB
Markdown
44 lines
2.4 KiB
Markdown
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Using GPU Kernels Tuning {#openvino_docs_IE_DG_GPU_Kernels_Tuning}
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======================
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GPU Kernels Tuning allows you to tune models, so the heavy computational layers are configured to fit better into
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hardware, which the tuning was done on. It is required to achieve best performance on GPU.
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> **NOTE** Currently only convolution and fully connected layers undergo tuning process. It means that the performance boost depends on the amount of that layers in the model.
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OpenVINO™ releases include the `<INSTALL_DIR>/inference_engine/bin/intel64/Release/cache.json` file with pretuned data for current state of the art models. It is highly recommended to do the
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tuning for new kind of models, hardwares or drivers.
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## Tuned data
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GPU tuning data is saved in JSON format.
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File's content is composed of 2 types of attributes and 1 type of value:
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1. Execution units number - this attribute splits the content into different EU sections.
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2. Hash - hashed tuned kernel data.
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Key: Array with kernel name and kernel's mode index.
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## Usage
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---
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You can activate Kernels Tuning process by setting `KEY_TUNING_MODE` flag to `TUNING_CREATE` and `KEY_TUNING_FILE` to `<"filename">` in a configuration map that is
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passed to the plugin while loading a network.
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This configuration modifies the behavior of the `ExecutableNetwork` object. Instead of standard network compilation, it will run the tuning process.
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Please keep in mind that the tuning can be very time consuming. The bigger the network, the longer it will take.
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File with tuned data is the result of this step.
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> **NOTE** If a filename passed to `KEY_TUNING_FILE` points to existing tuned data and you are tuning a new model, then this file will be extended by new data. This allows you to extend existing `cache.json` provided in the OpenVINO™ release package.
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The example below shows how to set and use the key files:
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```cpp
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Core ie;
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ie.SetConfig({{ CONFIG_KEY(TUNING_MODE), CONFIG_VALUE(TUNING_CREATE) }}, "GPU");
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ie.SetConfig({{ CONFIG_KEY(TUNING_FILE), "/path/to/tuning/file.json" }}, "GPU");
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// Further LoadNetwork calls will use the specified tuning parameters
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```
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---
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You can activate the inference with tuned data by setting `KEY_TUNING_MODE` flag to `TUNING_USE_EXISTING` and
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`KEY_TUNING_FILE` flag to `<"filename">`.
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GPU backend will process the content of the file during network compilation to configure the OpenCL kernels for the best performance.
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