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Update version to 0.10.0
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‎README.md‎

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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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<!-- Delete on release branches -->
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<!-- CORTEX_VERSION_README_MINOR x1 -->
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[install](https://www.cortex.dev/install) • [docs](https://www.cortex.dev) • [examples](https://github.com/cortexlabs/cortex/tree/0.9/examples) • [we're hiring](https://angel.co/cortex-labs-inc/jobs) • [email us](mailto:hello@cortex.dev) • [chat with us](https://gitter.im/cortexlabs/cortex)<br><br>
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<!-- Set header Cache-Control=no-cache on the S3 object metadata (see https://help.github.com/en/articles/about-anonymized-image-urls) -->
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![Demo](https://cortex-public.s3-us-west-2.amazonaws.com/demo/gif/v0.8.gif)<br>
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- kind: api
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name: generator
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model: s3://cortex-examples/text-generator/gpt-2/124M
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request_handler: handler.py
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tensorflow:
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model: s3://cortex-examples/tensorflow/text-generator/gpt-2/124M
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request_handler: handler.py
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compute:
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gpu: 1
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```
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<!-- CORTEX_VERSION_README_MINOR -->
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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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You can run the code that generated the model [here](https://colab.research.google.com/github/cortexlabs/cortex/blob/0.10/examples/tensorflow/text-generator/gpt-2.ipynb).
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<br>
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from encoder import get_encoder
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encoder = get_encoder()
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def pre_inference(sample, metadata):
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def pre_inference(sample, signature, metadata):
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context = encoder.encode(sample["text"])
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return {"context": [context]}
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def post_inference(prediction, metadata):
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def post_inference(prediction, signature, metadata):
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response = prediction["sample"]
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return encoder.decode(response)
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```
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## More examples
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<!-- CORTEX_VERSION_README_MINOR x3 -->
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- [Iris classification](https://github.com/cortexlabs/cortex/tree/0.9/examples/iris-classifier)
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- [Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.9/examples/sentiment-analysis) with BERT
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- [Image classification](https://github.com/cortexlabs/cortex/tree/0.9/examples/image-classifier) with Inception v3 and AlexNet
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<!-- CORTEX_VERSION_README_MINOR x4 -->
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- [Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.10/examples/tensorflow/sentiment-analysis) in TensorFlow with BERT
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- [Image classification](https://github.com/cortexlabs/cortex/tree/0.10/examples/tensorflow/image-classifier) in TensorFlow with Inception v3
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- [Text Generation](https://github.com/cortexlabs/cortex/tree/0.10/examples/pytorch/text-generator) in PyTorch with Hugging Face's DistilGPT2
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- [Iris classification](https://github.com/cortexlabs/cortex/tree/0.10/examples/xgboost/iris-classifier) in XGBoost / ONNX

‎build/build-image.sh‎

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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
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CORTEX_VERSION=master
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CORTEX_VERSION=0.10.0
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dir=$1
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image=$2

‎build/cli.sh‎

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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
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CORTEX_VERSION=master
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CORTEX_VERSION=0.10.0
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arg1=${1:-""}
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upload="false"

‎build/push-image.sh‎

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set -euo pipefail
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CORTEX_VERSION=master
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CORTEX_VERSION=0.10.0
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image=$1
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‎docs/cluster/config.md‎

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telemetry: true
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# Image paths
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image_predictor_serve: cortexlabs/predictor-serve:master
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image_predictor_serve_gpu: cortexlabs/predictor-serve-gpu:master
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image_tf_serve: cortexlabs/tf-serve:master
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image_tf_serve_gpu: cortexlabs/tf-serve-gpu:master
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image_tf_api: cortexlabs/tf-api:master
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image_onnx_serve: cortexlabs/onnx-serve:master
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image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:master
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image_operator: cortexlabs/operator:master
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image_manager: cortexlabs/manager:master
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image_downloader: cortexlabs/downloader:master
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image_cluster_autoscaler: cortexlabs/cluster-autoscaler:master
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image_metrics_server: cortexlabs/metrics-server:master
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image_nvidia: cortexlabs/nvidia:master
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image_fluentd: cortexlabs/fluentd:master
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image_statsd: cortexlabs/statsd:master
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image_istio_proxy: cortexlabs/istio-proxy:master
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image_istio_pilot: cortexlabs/istio-pilot:master
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image_istio_citadel: cortexlabs/istio-citadel:master
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image_istio_galley: cortexlabs/istio-galley:master
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image_predictor_serve: cortexlabs/predictor-serve:0.10.0
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image_predictor_serve_gpu: cortexlabs/predictor-serve-gpu:0.10.0
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image_tf_serve: cortexlabs/tf-serve:0.10.0
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image_tf_serve_gpu: cortexlabs/tf-serve-gpu:0.10.0
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image_tf_api: cortexlabs/tf-api:0.10.0
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image_onnx_serve: cortexlabs/onnx-serve:0.10.0
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image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:0.10.0
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image_operator: cortexlabs/operator:0.10.0
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image_manager: cortexlabs/manager:0.10.0
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image_downloader: cortexlabs/downloader:0.10.0
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image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.10.0
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image_metrics_server: cortexlabs/metrics-server:0.10.0
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image_nvidia: cortexlabs/nvidia:0.10.0
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image_fluentd: cortexlabs/fluentd:0.10.0
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image_statsd: cortexlabs/statsd:0.10.0
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image_istio_proxy: cortexlabs/istio-proxy:0.10.0
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image_istio_pilot: cortexlabs/istio-pilot:0.10.0
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image_istio_citadel: cortexlabs/istio-citadel:0.10.0
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image_istio_galley: cortexlabs/istio-galley:0.10.0
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```

‎docs/cluster/install.md‎

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<!-- CORTEX_VERSION_MINOR -->
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```bash
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# Install the Cortex CLI on your machine
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.10/get-cli.sh)"
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# Provision infrastructure on AWS and install Cortex
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cortex cluster up
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```bash
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# Clone the Cortex repository
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git clone -b master https://github.com/cortexlabs/cortex.git
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git clone -b 0.10 https://github.com/cortexlabs/cortex.git
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# Navigate to the iris classifier example
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cd cortex/examples/tensorflow/iris-classifier

‎docs/cluster/update.md‎

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cortex cluster down
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# Update your CLI
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.10/get-cli.sh)"
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# Confirm version
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cortex version

‎docs/deployments/python-client.md‎

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<!-- CORTEX_VERSION_BRANCH_STABLE, e.g. v0.9.0 -->
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```bash
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pip install git+https://github.com/cortexlabs/cortex.git@master#egg=cortex\&subdirectory=pkg/workloads/cortex/client
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pip install git+https://github.com/cortexlabs/cortex.git@v0.10.0#egg=cortex\&subdirectory=pkg/workloads/cortex/client
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```
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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 cluster info`.

‎docs/packaging/tensorflow.md‎

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# Packaging TensorFlow models
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<!-- CORTEX_VERSION_MINOR -->
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Export your trained model and upload the export directory, or a 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/tensorflow/sentiment-analysis)):
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Export your trained model and upload the export directory, or a 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/0.10/examples/tensorflow/sentiment-analysis)):
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```Python
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import tensorflow as tf

‎docs/summary.md‎

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* [Deploy machine learning models in production](../README.md)
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* [Install](cluster/install.md)
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* [GitHub](https://github.com/cortexlabs/cortex)
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* [Examples](https://github.com/cortexlabs/cortex/tree/master/examples) <!-- CORTEX_VERSION_MINOR -->
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* [Examples](https://github.com/cortexlabs/cortex/tree/0.10/examples) <!-- CORTEX_VERSION_MINOR -->
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* [We're hiring](https://angel.co/cortex-labs-inc/jobs)
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* [Email us](mailto:hello@cortex.dev)
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* [Chat with us](https://gitter.im/cortexlabs/cortex)

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