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docs/deployments/packaging-models.md

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## TensorFlow
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Export your trained model and upload the export directory, or checkpoint directory containing the export directory, which is usually the case if you used `estimator.train_and_evaluate`. An example is shown below (here is the [complete example](https://github.com/cortexlabs/cortex/blob/master/examples/sentiment-analysis)):
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```Python
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f.write(onnx_model.SerializeToString())
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```
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<!-- CORTEX_VERSION_MINOR -->
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Here are complete examples of converting models from some of the common ML frameworks to ONNX:
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* [PyTorch](https://github.com/cortexlabs/cortex/blob/master/examples/iris-classifier/models/pytorch_model.py)

examples/text-generator/gpt-2.ipynb

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"colab_type": "text"
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},
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"source": [
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"We also need to upload `vocab.bpe` and `encoder.json`, so that the [encoder](https://github.com/cortexlabs/cortex/blob/master/examples/text-generator/encoder.py) in the [pre-inference request handler](https://github.com/cortexlabs/cortex/blob/master/examples/text-generator/handler.py) can encode the input text before making a request to the model."
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