Update version to 0.15.0

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/0.14/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 "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.14/get-cli.sh)"
+$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.15/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/0.14/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.
+* [Sentiment analysis](https://github.com/cortexlabs/cortex/tree/0.15/examples/tensorflow/sentiment-analyzer): deploy a BERT model for sentiment analysis.
+* [Image classification](https://github.com/cortexlabs/cortex/tree/0.15/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
+* [Search completion](https://github.com/cortexlabs/cortex/tree/0.15/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
+* [Text generation](https://github.com/cortexlabs/cortex/tree/0.15/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
+* [Iris classification](https://github.com/cortexlabs/cortex/tree/0.15/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=0.15.0
 
 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=0.15.0
 
 arg1=${1:-""}
 upload="false"

build/push-image.sh

@@ -17,7 +17,7 @@
 
 set -euo pipefail
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.15.0
 
 image=$1
 

docs/cluster-management/config.md

@@ -42,7 +42,7 @@ 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/0.15/cluster-management/spot-instances for additional details on spot configuration
 spot: false
 ```
 
@@ -53,24 +53,24 @@ You can follow these [instructions](../deployments/system-packages.md) to build
 <!-- CORTEX_VERSION_BRANCH_STABLE -->
 ```yaml
 # 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:0.15.0
+image_python_serve_gpu: cortexlabs/python-serve-gpu:0.15.0
+image_tf_serve: cortexlabs/tf-serve:0.15.0
+image_tf_serve_gpu: cortexlabs/tf-serve-gpu:0.15.0
+image_tf_api: cortexlabs/tf-api:0.15.0
+image_onnx_serve: cortexlabs/onnx-serve:0.15.0
+image_onnx_serve_gpu: cortexlabs/onnx-serve-gpu:0.15.0
+image_operator: cortexlabs/operator:0.15.0
+image_manager: cortexlabs/manager:0.15.0
+image_downloader: cortexlabs/downloader:0.15.0
+image_request_monitor: cortexlabs/request-monitor:0.15.0
+image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.15.0
+image_metrics_server: cortexlabs/metrics-server:0.15.0
+image_nvidia: cortexlabs/nvidia:0.15.0
+image_fluentd: cortexlabs/fluentd:0.15.0
+image_statsd: cortexlabs/statsd:0.15.0
+image_istio_proxy: cortexlabs/istio-proxy:0.15.0
+image_istio_pilot: cortexlabs/istio-pilot:0.15.0
+image_istio_citadel: cortexlabs/istio-citadel:0.15.0
+image_istio_galley: cortexlabs/istio-galley:0.15.0
 ```

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/0.15/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 0.15 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/0.15/get-cli.sh)"
 
 # confirm version
 cortex version

docs/deployments/deployment.md

@@ -67,4 +67,4 @@ deleting my-api
 <!-- CORTEX_VERSION_MINOR -->
 * [Tutorial](../../examples/sklearn/iris-classifier/README.md) provides a step-by-step walkthough of deploying an iris classifier API
 * [CLI documentation](../cluster-management/cli.md) lists all CLI commands
-* [Examples](https://github.com/cortexlabs/cortex/tree/master/examples) demonstrate how to deploy models from common ML libraries
+* [Examples](https://github.com/cortexlabs/cortex/tree/0.15/examples) demonstrate how to deploy models from common ML libraries

docs/deployments/exporting.md

@@ -11,7 +11,7 @@ Here are examples for some common ML libraries:
 The recommended approach is export your PyTorch model with [torch.save()](https://pytorch.org/docs/stable/torch.html?highlight=save#torch.save). Here is PyTorch's documentation on [saving and loading models](https://pytorch.org/tutorials/beginner/saving_loading_models.html).
 
 <!-- CORTEX_VERSION_MINOR -->
-[examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/pytorch/iris-classifier) exports its trained model like this:
+[examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.15/examples/pytorch/iris-classifier) exports its trained model like this:
 
 ```python
 torch.save(model.state_dict(), "weights.pth")
@@ -22,7 +22,7 @@ torch.save(model.state_dict(), "weights.pth")
 It may also be possible to export your PyTorch model into the ONNX format using [torch.onnx.export()](https://pytorch.org/docs/stable/onnx.html#torch.onnx.export).
 
 <!-- CORTEX_VERSION_MINOR -->
-For example, if [examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/pytorch/iris-classifier) were to export the model to ONNX, it would look like this:
+For example, if [examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.15/examples/pytorch/iris-classifier) were to export the model to ONNX, it would look like this:
 
 ```python
 placeholder = torch.randn(1, 4)
@@ -50,7 +50,7 @@ A TensorFlow `SavedModel` directory should have this structure:
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-Most of the TensorFlow examples use this approach. Here is the relevant code from [examples/tensorflow/sentiment-analyzer](https://github.com/cortexlabs/cortex/blob/master/examples/tensorflow/sentiment-analyzer):
+Most of the TensorFlow examples use this approach. Here is the relevant code from [examples/tensorflow/sentiment-analyzer](https://github.com/cortexlabs/cortex/blob/0.15/examples/tensorflow/sentiment-analyzer):
 
 ```python
 import tensorflow as tf
@@ -88,14 +88,14 @@ aws s3 cp bert.zip s3://my-bucket/bert.zip
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-[examples/tensorflow/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/tensorflow/iris-classifier) also use the `SavedModel` approach, and includes a Python notebook demonstrating how it was exported.
+[examples/tensorflow/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.15/examples/tensorflow/iris-classifier) also use the `SavedModel` approach, and includes a Python notebook demonstrating how it was exported.
 
 ### Other model formats
 
 There are other ways to export Keras or TensorFlow models, and as long as they can be loaded and used to make predictions in Python, they will be supported by Cortex.
 
 <!-- CORTEX_VERSION_MINOR -->
-For example, the `crnn` API in [examples/tensorflow/license-plate-reader](https://github.com/cortexlabs/cortex/blob/master/examples/tensorflow/license-plate-reader) uses this approach.
+For example, the `crnn` API in [examples/tensorflow/license-plate-reader](https://github.com/cortexlabs/cortex/blob/0.15/examples/tensorflow/license-plate-reader) uses this approach.
 
 ## Scikit-learn
 
@@ -104,7 +104,7 @@ For example, the `crnn` API in [examples/tensorflow/license-plate-reader](https:
 Scikit-learn models are typically exported using `pickle`. Here is [Scikit-learn's documentation](https://scikit-learn.org/stable/modules/model_persistence.html).
 
 <!-- CORTEX_VERSION_MINOR -->
-[examples/sklearn/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/sklearn/iris-classifier) uses this approach. Here is the relevant code:
+[examples/sklearn/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.15/examples/sklearn/iris-classifier) uses this approach. Here is the relevant code:
 
 ```python
 pickle.dump(model, open("model.pkl", "wb"))
@@ -157,7 +157,7 @@ model.save_model("model.bin")
 It is also possible to export an XGBoost model to the ONNX format using [onnxmltools](https://github.com/onnx/onnxmltools).
 
 <!-- CORTEX_VERSION_MINOR -->
-[examples/xgboost/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/xgboost/iris-classifier) uses this approach. Here is the relevant code:
+[examples/xgboost/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.15/examples/xgboost/iris-classifier) uses this approach. Here is the relevant code:
 
 ```python
 from onnxmltools.convert import convert_xgboost

docs/deployments/predictors.md

@@ -68,10 +68,10 @@ For proper separation of concerns, it is recommended to use the constructor's `c
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-Many of the [examples](https://github.com/cortexlabs/cortex/tree/master/examples) use the Python Predictor, including all of the PyTorch examples.
+Many of the [examples](https://github.com/cortexlabs/cortex/tree/0.15/examples) use the Python Predictor, including all of the PyTorch examples.
 
 <!-- CORTEX_VERSION_MINOR -->
-Here is the Predictor for [examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/tree/master/examples/pytorch/iris-classifier):
+Here is the Predictor for [examples/pytorch/iris-classifier](https://github.com/cortexlabs/cortex/tree/0.15/examples/pytorch/iris-classifier):
 
 ```python
 import re
@@ -150,7 +150,7 @@ xgboost==0.90
 ```
 
 <!-- CORTEX_VERSION_MINOR x2 -->
-The pre-installed system packages are listed in [images/python-serve/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-serve/Dockerfile) (for CPU) or [images/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 [images/python-serve/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.15/images/python-serve/Dockerfile) (for CPU) or [images/python-serve-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.15/images/python-serve-gpu/Dockerfile) (for GPU).
 
 If your application requires additional dependencies, you can install additional [Python packages](python-packages.md) and [system packages](system-packages.md).
 
@@ -183,17 +183,17 @@ class TensorFlowPredictor:
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-Cortex provides a `tensorflow_client` to your Predictor's constructor. `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 to make predictions using your model. It should be saved as an instance variable in your Predictor, and your `predict()` function should call `tensorflow_client.predict()` to make an inference with 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` to your Predictor's constructor. `tensorflow_client` is an instance of [TensorFlowClient](https://github.com/cortexlabs/cortex/tree/0.15/pkg/workloads/cortex/lib/client/tensorflow.py) that manages a connection to a TensorFlow Serving container to make predictions using your model. It should be saved as an instance variable in your Predictor, and your `predict()` function should call `tensorflow_client.predict()` to make an inference with your exported TensorFlow model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
 
 For proper separation of concerns, it is recommended to use the constructor's `config` paramater for information such as configurable model parameters or download links for initialization files. You define `config` in your [API configuration](api-configuration.md), and it is passed through to your Predictor's constructor.
 
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-Most of the examples in [examples/tensorflow](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow) use the TensorFlow Predictor.
+Most of the examples in [examples/tensorflow](https://github.com/cortexlabs/cortex/tree/0.15/examples/tensorflow) use the TensorFlow Predictor.
 
 <!-- CORTEX_VERSION_MINOR -->
-Here is the Predictor for [examples/tensorflow/iris-classifier](https://github.com/cortexlabs/cortex/tree/master/examples/tensorflow/iris-classifier):
+Here is the Predictor for [examples/tensorflow/iris-classifier](https://github.com/cortexlabs/cortex/tree/0.15/examples/tensorflow/iris-classifier):
 
 ```python
 labels = ["setosa", "versicolor", "virginica"]
@@ -226,7 +226,7 @@ tensorflow==2.1.0
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-The pre-installed system packages are listed in [images/tf-api/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/tf-api/Dockerfile).
+The pre-installed system packages are listed in [images/tf-api/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.15/images/tf-api/Dockerfile).
 
 If your application requires additional dependencies, you can install additional [Python packages](python-packages.md) and [system packages](system-packages.md).
 
@@ -259,14 +259,14 @@ class ONNXPredictor:
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-Cortex provides an `onnx_client` to your Predictor's constructor. `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 to make predictions using your model. It should be saved as an instance variable in your Predictor, and your `predict()` function should call `onnx_client.predict()` to make an inference with 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` to your Predictor's constructor. `onnx_client` is an instance of [ONNXClient](https://github.com/cortexlabs/cortex/tree/0.15/pkg/workloads/cortex/lib/client/onnx.py) that manages an ONNX Runtime session to make predictions using your model. It should be saved as an instance variable in your Predictor, and your `predict()` function should call `onnx_client.predict()` to make an inference with your exported ONNX model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your `predict()` function as well.
 
 For proper separation of concerns, it is recommended to use the constructor's `config` paramater for information such as configurable model parameters or download links for initialization files. You define `config` in your [API configuration](api-configuration.md), and it is passed through to your Predictor's constructor.
 
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-[examples/xgboost/iris-classifier](https://github.com/cortexlabs/cortex/tree/master/examples/xgboost/iris-classifier) uses the ONNX Predictor:
+[examples/xgboost/iris-classifier](https://github.com/cortexlabs/cortex/tree/0.15/examples/xgboost/iris-classifier) uses the ONNX Predictor:
 
 ```python
 labels = ["setosa", "versicolor", "virginica"]
@@ -303,6 +303,6 @@ requests==2.22.0
 ```
 
 <!-- CORTEX_VERSION_MINOR x2 -->
-The pre-installed system packages are listed in [images/onnx-serve/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/onnx-serve/Dockerfile) (for CPU) or [images/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 [images/onnx-serve/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.15/images/onnx-serve/Dockerfile) (for CPU) or [images/onnx-serve-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.15/images/onnx-serve-gpu/Dockerfile) (for GPU).
 
 If your application requires additional dependencies, you can install additional [Python packages](python-packages.md) and [system packages](system-packages.md).