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deliahudeliahu
committedMar 5, 2020
Update version to 0.14.0
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v0.14.1v0.14.0
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‎README.md

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<br>
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<!-- Delete on release branches -->
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<!-- CORTEX_VERSION_README_MINOR -->
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[install](https://cortex.dev/install) • [tutorial](https://cortex.dev/iris-classifier) • [docs](https://cortex.dev) • [examples](https://github.com/cortexlabs/cortex/tree/0.13/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://d1zqebknpdh033.cloudfront.net/demo/gif/v0.13_2.gif)
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<!-- CORTEX_VERSION_README_MINOR -->
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```bash
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# install the CLI on your machine
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$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.13/get-cli.sh)"
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$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
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# provision infrastructure on AWS and spin up a cluster
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$ 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.

‎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=0.14.0
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dir=$1
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image=$2

‎build/cli.sh

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ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
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CORTEX_VERSION=master
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CORTEX_VERSION=0.14.0
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arg1=${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=0.14.0
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image=$1
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‎docs/cluster-management/config.md

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log_group: cortex
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# whether to use spot instances in the cluster (default: false)
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# see https://cortex.dev/v/master/cluster-management/spot-instances for additional details on spot configuration
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# see https://cortex.dev/v/0.14/cluster-management/spot-instances for additional details on spot configuration
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spot: false
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# docker image paths
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image_python_serve: cortexlabs/python-serve:master
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image_python_serve_gpu: cortexlabs/python-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_request_monitor: cortexlabs/request-monitor: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_python_serve: cortexlabs/python-serve:0.14.0
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image_python_serve_gpu: cortexlabs/python-serve-gpu:0.14.0
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image_tf_serve: cortexlabs/tf-serve:0.14.0
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image_tf_serve_gpu: cortexlabs/tf-serve-gpu:0.14.0
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image_tf_api: cortexlabs/tf-api:0.14.0
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image_onnx_serve: cortexlabs/onnx-serve:0.14.0
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image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:0.14.0
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image_operator: cortexlabs/operator:0.14.0
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image_manager: cortexlabs/manager:0.14.0
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image_downloader: cortexlabs/downloader:0.14.0
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image_request_monitor: cortexlabs/request-monitor:0.14.0
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image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.14.0
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image_metrics_server: cortexlabs/metrics-server:0.14.0
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image_nvidia: cortexlabs/nvidia:0.14.0
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image_fluentd: cortexlabs/fluentd:0.14.0
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image_statsd: cortexlabs/statsd:0.14.0
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image_istio_proxy: cortexlabs/istio-proxy:0.14.0
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image_istio_pilot: cortexlabs/istio-pilot:0.14.0
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image_istio_citadel: cortexlabs/istio-citadel:0.14.0
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image_istio_galley: cortexlabs/istio-galley:0.14.0
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```

‎docs/cluster-management/install.md

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<!-- CORTEX_VERSION_MINOR -->
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```bash
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# install the 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 "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
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# provision infrastructure on AWS and spin up a cluster
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$ cortex cluster up
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```bash
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git clone -b master https://github.com/cortexlabs/cortex.git
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git clone -b 0.14 https://github.com/cortexlabs/cortex.git
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‎docs/cluster-management/update.md

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cortex cluster down
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# update your CLI
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
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bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
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# confirm version
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cortex version

‎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).

‎docs/deployments/python.md

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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).

‎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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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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‎docs/packaging-models/tensorflow.md

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# TensorFlow
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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-analyzer)):
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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/0.14/examples/tensorflow/sentiment-analyzer)):
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```python
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import tensorflow as tf

‎docs/summary.md

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* [Install](cluster-management/install.md)
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* [Tutorial](../examples/sklearn/iris-classifier/README.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/0.14/examples) <!-- CORTEX_VERSION_MINOR -->
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* [Chat with us](https://gitter.im/cortexlabs/cortex)
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* [Email us](mailto:hello@cortex.dev)
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* [We're hiring](https://angel.co/cortex-labs-inc/jobs)

‎examples/tensorflow/image-classifier/inception.ipynb

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"<!-- CORTEX_VERSION_MINOR -->\n",
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow/image-classifier) for how to deploy the model as an API."
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/image-classifier) for how to deploy the model as an API."
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‎examples/tensorflow/iris-classifier/tensorflow.ipynb

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"<!-- CORTEX_VERSION_MINOR -->\n",
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow/iris-classifier) for how to deploy the model as an API."
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/iris-classifier) for how to deploy the model as an API."
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‎examples/tensorflow/sentiment-analyzer/bert.ipynb

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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow/sentiment-analyzer) for how to deploy the model as an API."
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/sentiment-analyzer) for how to deploy the model as an API."
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‎examples/tensorflow/text-generator/gpt-2.ipynb

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"<!-- CORTEX_VERSION_MINOR x2 -->\n",
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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/tensorflow/text-generator/encoder.py) in the [Predictor](https://github.com/cortexlabs/cortex/blob/master/examples/tensorflow/text-generator/predictor.py) can encode the input text before making a request to the model."
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"We also need to upload `vocab.bpe` and `encoder.json`, so that the [encoder](https://github.com/cortexlabs/cortex/blob/0.14/examples/tensorflow/text-generator/encoder.py) in the [Predictor](https://github.com/cortexlabs/cortex/blob/0.14/examples/tensorflow/text-generator/predictor.py) can encode the input text before making a request to the model."
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow/text-generator) for how to deploy the model as an API."
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"That's it! See the [example on GitHub](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/text-generator) for how to deploy the model as an API."
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‎examples/xgboost/iris-classifier/xgboost.ipynb

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"That's it! See the [example](https://github.com/cortexlabs/cortex/tree/master/examples/xgboost/iris-classifier) for how to deploy the model as an API."
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"That's it! See the [example](https://github.com/cortexlabs/cortex/tree/0.14/examples/xgboost/iris-classifier) for how to deploy the model as an API."
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‎get-cli.sh

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set -e
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CORTEX_VERSION_BRANCH_STABLE=master
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CORTEX_VERSION_BRANCH_STABLE=0.14.0
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case "$OSTYPE" in
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darwin*) parsed_os="darwin" ;;

‎manager/install.sh

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set -e
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CORTEX_VERSION=0.14.0
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EKSCTL_TIMEOUT=45m
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arg1="$1"
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function ensure_eks() {
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# Cluster statuses: https://github.com/aws/aws-sdk-go/blob/master/service/eks/api.go#L2785
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set +e
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‎pkg/consts/consts.go

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var (
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CortexVersionMinor = "master" // CORTEX_VERSION_MINOR
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CortexVersion = "0.14.0" // CORTEX_VERSION
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CortexVersionMinor = "0.14" // CORTEX_VERSION_MINOR
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MaxClassesPerTrackerRequest = 20 // cloudwatch.GeMetricData can get up to 100 metrics per request, avoid multiple requests and have room for other stats
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‎pkg/workloads/cortex/client/cortex/client.py

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@@ -44,7 +44,7 @@ def __init__(self, aws_access_key_id, aws_secret_access_key, operator_url):
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self.aws_access_key_id = aws_access_key_id
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self.aws_secret_access_key = aws_secret_access_key
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self.headers = {
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"CortexAPIVersion": "master", # CORTEX_VERSION
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"CortexAPIVersion": "0.14.0", # CORTEX_VERSION
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"Authorization": "CortexAWS {}|{}".format(
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self.aws_access_key_id, self.aws_secret_access_key
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),

‎pkg/workloads/cortex/client/setup.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -16,7 +16,7 @@
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setup(
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name="cortex",
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version="master", # CORTEX_VERSION
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version="0.14.0", # CORTEX_VERSION
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description="",
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author="Cortex Labs",
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author_email="dev@cortexlabs.com",

‎pkg/workloads/cortex/consts.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -12,4 +12,4 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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CORTEX_VERSION = "master"
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CORTEX_VERSION = "0.14.0"

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