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Cortex is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command. <br>
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### Step 1: Configure your deployment
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<!-- CORTEX_VERSION_README_MINOR -->
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Define a `deployment` and an `api` resource. A `deployment` specifies a set of APIs that are deployed together. An `api` makes a model available as a web service that can serve real-time predictions. The configuration below will download the model from the `cortex-examples` S3 bucket. You can run the code that generated the model [here](https://colab.research.google.com/github/cortexlabs/cortex/blob/0.8/examples/text-generator/gpt-2.ipynb).
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Define a `deployment` and an `api` resource. A `deployment` specifies a set of APIs that are deployed together. An `api` makes a model available as a web service that can serve real-time predictions. The configuration below will download the model from the `cortex-examples` S3 bucket. You can run the code that generated the model [here](https://colab.research.google.com/github/cortexlabs/cortex/blob/0.9/examples/text-generator/gpt-2.ipynb).
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```yaml
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# cortex.yaml
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The Python client needs to be initialized with AWS credentials and an operator URL for your Cortex cluster. You can find the operator URL by running `./cortex.sh endpoints`.
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