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# provision infrastructure on AWS and spin up a cluster
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$ cortex cluster up
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## Examples of Cortex deployments
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<!-- CORTEX_VERSION_README_MINOR x5 -->
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*[Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.13/examples/tensorflow/sentiment-analyzer): deploy a BERT model for sentiment analysis.
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*[Image classification](https://github.com/cortexlabs/cortex/tree/0.13/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
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*[Search completion](https://github.com/cortexlabs/cortex/tree/0.13/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
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*[Text generation](https://github.com/cortexlabs/cortex/tree/0.13/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
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*[Iris classification](https://github.com/cortexlabs/cortex/tree/0.13/examples/sklearn/iris-classifier): deploy a scikit-learn model to classify iris flowers.
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*[Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/sentiment-analyzer): deploy a BERT model for sentiment analysis.
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*[Image classification](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
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*[Search completion](https://github.com/cortexlabs/cortex/tree/0.14/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
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*[Text generation](https://github.com/cortexlabs/cortex/tree/0.14/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
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*[Iris classification](https://github.com/cortexlabs/cortex/tree/0.14/examples/sklearn/iris-classifier): deploy a scikit-learn model to classify iris flowers.
Copy file name to clipboardExpand all lines: docs/deployments/onnx.md
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An ONNX Predictor is a Python class that describes how to serve your ONNX model to make predictions.
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<!-- CORTEX_VERSION_MINOR -->
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Cortex provides an `onnx_client` and a config object to initialize your implementation of the ONNX Predictor class. The `onnx_client` is an instance of [ONNXClient](https://github.com/cortexlabs/cortex/tree/master/pkg/workloads/cortex/lib/client/onnx.py) that manages an ONNX Runtime session and helps make predictions using your model. Once your implementation of the ONNX Predictor class has been initialized, the replica is available to serve requests. Upon receiving a request, your implementation's `predict()` function is called with the JSON payload and is responsible for returning a prediction or batch of predictions. Your `predict()` function should call `onnx_client.predict()` to make an inference against your exported ONNX model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
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Cortex provides an `onnx_client` and a config object to initialize your implementation of the ONNX Predictor class. The `onnx_client` is an instance of [ONNXClient](https://github.com/cortexlabs/cortex/tree/0.14/pkg/workloads/cortex/lib/client/onnx.py) that manages an ONNX Runtime session and helps make predictions using your model. Once your implementation of the ONNX Predictor class has been initialized, the replica is available to serve requests. Upon receiving a request, your implementation's `predict()` function is called with the JSON payload and is responsible for returning a prediction or batch of predictions. Your `predict()` function should call `onnx_client.predict()` to make an inference against your exported ONNX model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
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## Implementation
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```
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<!-- CORTEX_VERSION_MINOR x2 -->
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The pre-installed system packages are listed in the [onnx-serve Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/onnx-serve/Dockerfile) (for CPU) or the [onnx-serve-gpu Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/onnx-serve-gpu/Dockerfile) (for GPU).
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The pre-installed system packages are listed in the [onnx-serve Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/onnx-serve/Dockerfile) (for CPU) or the [onnx-serve-gpu Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/onnx-serve-gpu/Dockerfile) (for GPU).
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If your application requires additional dependencies, you can [install additional Python packages](../dependency-management/python-packages.md) or [install additional system packages](../dependency-management/system-packages.md).
Copy file name to clipboardExpand all lines: docs/deployments/python.md
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```
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<!-- CORTEX_VERSION_MINOR x2 -->
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The pre-installed system packages are listed in the [python-serve Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-serve/Dockerfile) (for CPU) or the [python-serve-gpu Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-serve-gpu/Dockerfile) (for GPU).
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The pre-installed system packages are listed in the [python-serve Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/python-serve/Dockerfile) (for CPU) or the [python-serve-gpu Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/python-serve-gpu/Dockerfile) (for GPU).
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If your application requires additional dependencies, you can [install additional Python packages](../dependency-management/python-packages.md) or [install additional system packages](../dependency-management/system-packages.md).
Copy file name to clipboardExpand all lines: docs/deployments/tensorflow.md
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A TensorFlow Predictor is a Python class that describes how to serve your TensorFlow model to make predictions.
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Cortex provides a `tensorflow_client` and a config object to initialize your implementation of the TensorFlow Predictor class. The `tensorflow_client` is an instance of [TensorFlowClient](https://github.com/cortexlabs/cortex/tree/master/pkg/workloads/cortex/lib/client/tensorflow.py) that manages a connection to a TensorFlow Serving container via gRPC to make predictions using your model. Once your implementation of the TensorFlow Predictor class has been initialized, the replica is available to serve requests. Upon receiving a request, your implementation's `predict()` function is called with the JSON payload and is responsible for returning a prediction or batch of predictions. Your `predict()` function should call `tensorflow_client.predict()` to make an inference against your exported TensorFlow model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
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Cortex provides a `tensorflow_client` and a config object to initialize your implementation of the TensorFlow Predictor class. The `tensorflow_client` is an instance of [TensorFlowClient](https://github.com/cortexlabs/cortex/tree/0.14/pkg/workloads/cortex/lib/client/tensorflow.py) that manages a connection to a TensorFlow Serving container via gRPC to make predictions using your model. Once your implementation of the TensorFlow Predictor class has been initialized, the replica is available to serve requests. Upon receiving a request, your implementation's `predict()` function is called with the JSON payload and is responsible for returning a prediction or batch of predictions. Your `predict()` function should call `tensorflow_client.predict()` to make an inference against your exported TensorFlow model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
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## Implementation
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```
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<!-- CORTEX_VERSION_MINOR -->
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The pre-installed system packages are listed in the [tf-api Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/tf-api/Dockerfile).
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The pre-installed system packages are listed in the [tf-api Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/tf-api/Dockerfile).
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If your application requires additional dependencies, you can [install additional Python packages](../dependency-management/python-packages.md) or [install additional system packages](../dependency-management/system-packages.md).
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