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* remove implicit linking of tensorrt and dnnl ep shared libs (#5262) * Update DirectML Nuget to 1.3.0 (#5274) * Update PyTorch TransformerModel sample (#5275) * Insert telemetry template into GPU build, add telemry build switches. (#5278) * Synchronize training dependency versions between Docker image and Python wheel (#5261) * Downgrade GCC (#5269) * Remove --enable_symbolic_shape_infer_tests to fix linux ci pipeline build error. Co-authored-by: Edward Chen Co-authored-by: George Wu <jywu@microsoft.com> Co-authored-by: Dwayne Robinson <dwayner@microsoft.com> Co-authored-by: Thiago Crepaldi <thiago.crepaldi@microsoft.com> Co-authored-by: Dmitri Smirnov <yuslepukhin@users.noreply.github.com> Co-authored-by: edgchen1 <18449977+edgchen1@users.noreply.github.com> Co-authored-by: Changming Sun <chasun@microsoft.com>
33 lines
1.2 KiB
Markdown
33 lines
1.2 KiB
Markdown
# TransformerModel example
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This example was adapted from Pytorch's [Sequence-to-Sequence Modeling with nn.Transformer and TorchText](https://pytorch.org/tutorials/beginner/transformer_tutorial.html) tutorial
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## Requirements
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* PyTorch 1.6+
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* TorchText 0.6+
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* ONNX Runtime 1.5+
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## Running PyTorch version
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```bash
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python pt_model.py
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```
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## Running ONNX Runtime version
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```bash
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python ort_model.py
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```
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## Optional arguments
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| Argument | Description | Default |
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| :---------------- | :-----------------------------------------------------: | --------: |
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| --batch-size | input batch size for training | 20 |
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| --test-batch-size | input batch size for testing | 20 |
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| --epochs | number of epochs to train | 2 |
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| --lr | learning rate | 0.001 |
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| --no-cuda | disables CUDA training | False |
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| --seed | random seed | 1 |
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| --log-interval | how many batches to wait before logging training status | 200 |
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