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CITATION.cff
cff-version: 1.2.0 message: "If you use TensorFlow in your research, please cite it using these metadata. Software is available from tensorflow.org." title: TensorFlow, Large-scale machine learning on heterogeneous systems
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docs/es/docs/async.md
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docs/en/data/external_links.yml
title: 'Uber: Ludwig v0.2 Adds New Features and Other Improvements to its Deep Learning Toolbox [including a FastAPI server]' - author: Maarten Grootendorst author_link: https://www.linkedin.com/in/mgrootendorst/ link: https://towardsdatascience.com/how-to-deploy-a-machine-learning-model-dc51200fe8cf title: How to Deploy a Machine Learning Model - author: Johannes Gontrum author_link: https://twitter.com/gntrm
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docs/en/docs/advanced/index.md
Some course providers ✨ [**sponsor FastAPI**](../help-fastapi.md#sponsor-the-author){.internal-link target=_blank} ✨, this ensures the continued and healthy **development** of FastAPI and its **ecosystem**.
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docs/pt/docs/async.md
* **Machine Learning**: Normalmente exige muita multiplicação de matrizes e vetores. Pense numa grande folha de papel com números e multiplicando todos eles juntos e ao mesmo tempo.
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docs/fr/docs/async.md
* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.
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docs/en/docs/advanced/events.md
## Use Case Let's start with an example **use case** and then see how to solve it with this. Let's imagine that you have some **machine learning models** that you want to use to handle requests. 🤖
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docs/en/docs/async.md
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SECURITY.md
## TensorFlow models are programs TensorFlow [**models**](https://developers.google.com/machine-learning/glossary/#model) (to use a term commonly used by machine learning practitioners) are expressed as programs that TensorFlow executes. TensorFlow programs are encoded as computation [**graphs**](https://developers.google.com/machine-learning/glossary/#graph). Since models are practically programs that TensorFlow executes, using untrusted
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docs_src/path_params/tutorial005.py
app = FastAPI() @app.get("/models/{model_name}") async def get_model(model_name: ModelName): if model_name is ModelName.alexnet: return {"model_name": model_name, "message": "Deep Learning FTW!"} if model_name.value == "lenet": return {"model_name": model_name, "message": "LeCNN all the images"}
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