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# CLI | ||
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## Exporting models to ONNX | ||
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If you wish to run inference via usage of the ONNXruntime, models can be exported to onnx using the `micro_dl/cli/onnx_export_script.py`. See below for an example usage of this script with 5-input-stack model: | ||
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```bash | ||
python micro_dl/cli/onnx_export_script.py --model_path path/to/your/pt_model.pt --stack_depth 5 --export_path intended/path/to/model/export.onnx --test_input path/to/test/input.npy | ||
``` | ||
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**Some Notes:** | ||
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* For cpu sharing reasons, running an onnx model requires a dedicated node on hpc OR a non-distributed system (for example a personal laptop or other device). | ||
* Test inputs are optional, but help verify that the exported model can be run if exporting from intended usage device. | ||
* Models must be located in a lighting training logs directory with a valid `config.yaml` in order to be initialized. This can be "hacked" by locating the config in a directory called `checkpoints` beneath a valid config's directory. |
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