Update version to 0.14.0

Author: deliahu

README.md

@@ -4,10 +4,6 @@ Cortex is an open source platform for deploying machine learning models as produ
 
 <br>
 
-<!-- Delete on release branches -->
-<!-- CORTEX_VERSION_README_MINOR -->
-[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:[email protected]) • [chat with us](https://gitter.im/cortexlabs/cortex)<br><br>
-
 <!-- Set header Cache-Control=no-cache on the S3 object metadata (see https://help.github.com/en/articles/about-anonymized-image-urls) -->
 ![Demo](https://d1zqebknpdh033.cloudfront.net/demo/gif/v0.13_2.gif)
 
@@ -33,7 +29,7 @@ Cortex is designed to be self-hosted on any AWS account. You can spin up a clust
 <!-- CORTEX_VERSION_README_MINOR -->
 ```bash
 # install the CLI on your machine
-$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.13/get-cli.sh)"
+$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
 
 # provision infrastructure on AWS and spin up a cluster
 $ cortex cluster up
@@ -140,8 +136,8 @@ The CLI sends configuration and code to the cluster every time you run `cortex d
 ## Examples of Cortex deployments
 
 <!-- CORTEX_VERSION_README_MINOR x5 -->
-* [Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.13/examples/tensorflow/sentiment-analyzer): deploy a BERT model for sentiment analysis.
-* [Image classification](https://github.com/cortexlabs/cortex/tree/0.13/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
-* [Search completion](https://github.com/cortexlabs/cortex/tree/0.13/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
-* [Text generation](https://github.com/cortexlabs/cortex/tree/0.13/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
-* [Iris classification](https://github.com/cortexlabs/cortex/tree/0.13/examples/sklearn/iris-classifier): deploy a scikit-learn model to classify iris flowers.
+* [Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/sentiment-analyzer): deploy a BERT model for sentiment analysis.
+* [Image classification](https://github.com/cortexlabs/cortex/tree/0.14/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
+* [Search completion](https://github.com/cortexlabs/cortex/tree/0.14/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
+* [Text generation](https://github.com/cortexlabs/cortex/tree/0.14/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
+* [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

@@ -19,7 +19,7 @@ set -euo pipefail
 
 ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.14.0
 
 dir=$1
 image=$2

build/cli.sh

@@ -19,7 +19,7 @@ set -euo pipefail
 
 ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.14.0
 
 arg1=${1:-""}
 upload="false"

build/push-image.sh

@@ -17,7 +17,7 @@
 
 set -euo pipefail
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.14.0
 
 image=$1
 

docs/cluster-management/config.md

@@ -43,28 +43,28 @@ instance_volume_size: 50
 log_group: cortex
 
 # whether to use spot instances in the cluster (default: false)
-# see https://cortex.dev/v/master/cluster-management/spot-instances for additional details on spot configuration
+# see https://cortex.dev/v/0.14/cluster-management/spot-instances for additional details on spot configuration
 spot: false
 
 # docker image paths
-image_python_serve: cortexlabs/python-serve:master
-image_python_serve_gpu: cortexlabs/python-serve-gpu:master
-image_tf_serve: cortexlabs/tf-serve:master
-image_tf_serve_gpu: cortexlabs/tf-serve-gpu:master
-image_tf_api: cortexlabs/tf-api:master
-image_onnx_serve: cortexlabs/onnx-serve:master
-image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:master
-image_operator: cortexlabs/operator:master
-image_manager: cortexlabs/manager:master
-image_downloader: cortexlabs/downloader:master
-image_request_monitor: cortexlabs/request-monitor:master
-image_cluster_autoscaler: cortexlabs/cluster-autoscaler:master
-image_metrics_server: cortexlabs/metrics-server:master
-image_nvidia: cortexlabs/nvidia:master
-image_fluentd: cortexlabs/fluentd:master
-image_statsd: cortexlabs/statsd:master
-image_istio_proxy: cortexlabs/istio-proxy:master
-image_istio_pilot: cortexlabs/istio-pilot:master
-image_istio_citadel: cortexlabs/istio-citadel:master
-image_istio_galley: cortexlabs/istio-galley:master
+image_python_serve: cortexlabs/python-serve:0.14.0
+image_python_serve_gpu: cortexlabs/python-serve-gpu:0.14.0
+image_tf_serve: cortexlabs/tf-serve:0.14.0
+image_tf_serve_gpu: cortexlabs/tf-serve-gpu:0.14.0
+image_tf_api: cortexlabs/tf-api:0.14.0
+image_onnx_serve: cortexlabs/onnx-serve:0.14.0
+image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:0.14.0
+image_operator: cortexlabs/operator:0.14.0
+image_manager: cortexlabs/manager:0.14.0
+image_downloader: cortexlabs/downloader:0.14.0
+image_request_monitor: cortexlabs/request-monitor:0.14.0
+image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.14.0
+image_metrics_server: cortexlabs/metrics-server:0.14.0
+image_nvidia: cortexlabs/nvidia:0.14.0
+image_fluentd: cortexlabs/fluentd:0.14.0
+image_statsd: cortexlabs/statsd:0.14.0
+image_istio_proxy: cortexlabs/istio-proxy:0.14.0
+image_istio_pilot: cortexlabs/istio-pilot:0.14.0
+image_istio_citadel: cortexlabs/istio-citadel:0.14.0
+image_istio_galley: cortexlabs/istio-galley:0.14.0
 ```

docs/cluster-management/install.md

@@ -12,7 +12,7 @@ See [cluster configuration](config.md) to learn how you can customize your clust
 <!-- CORTEX_VERSION_MINOR -->
 ```bash
 # install the CLI on your machine
-$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
+$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
 
 # provision infrastructure on AWS and spin up a cluster
 $ cortex cluster up
@@ -38,7 +38,7 @@ your cluster is ready!
 
 ```bash
 # clone the Cortex repository
-git clone -b master https://github.com/cortexlabs/cortex.git
+git clone -b 0.14 https://github.com/cortexlabs/cortex.git
 
 # navigate to the TensorFlow iris classification example
 cd cortex/examples/tensorflow/iris-classifier

docs/cluster-management/update.md

@@ -22,7 +22,7 @@ cortex cluster update
 cortex cluster down
 
 # update your CLI
-bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
+bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
 
 # confirm version
 cortex version

docs/deployments/onnx.md

@@ -67,7 +67,7 @@ You can log information about each request by adding a `?debug=true` parameter t
 An ONNX Predictor is a Python class that describes how to serve your ONNX model to make predictions.
 
 <!-- CORTEX_VERSION_MINOR -->
-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.
+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.
 
 ## Implementation
 
@@ -133,6 +133,6 @@ requests==2.22.0
 ```
 
 <!-- CORTEX_VERSION_MINOR x2 -->
-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).
+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).
 
 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

@@ -171,6 +171,6 @@ xgboost==0.90
 ```
 
 <!-- CORTEX_VERSION_MINOR x2 -->
-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).
+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).
 
 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

@@ -68,7 +68,7 @@ You can log information about each request by adding a `?debug=true` parameter t
 A TensorFlow Predictor is a Python class that describes how to serve your TensorFlow model to make predictions.
 
 <!-- CORTEX_VERSION_MINOR -->
-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.
+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.
 
 ## Implementation
 
@@ -128,6 +128,6 @@ tensorflow==2.1.0
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-The pre-installed system packages are listed in the [tf-api Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/tf-api/Dockerfile).
+The pre-installed system packages are listed in the [tf-api Dockerfile](https://github.com/cortexlabs/cortex/tree/0.14/images/tf-api/Dockerfile).
 
 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).