Update version to 0.18.0

Author: deliahu

README.md

@@ -1,14 +1,7 @@
-<!-- Delete on release branches -->
-<img src='https://s3-us-west-2.amazonaws.com/cortex-public/logo.png' height='88'>
-
 # Build machine learning APIs
 
 <br>
 
-<!-- Delete on release branches -->
-<!-- CORTEX_VERSION_README_MINOR -->
-[install](https://docs.cortex.dev/install) • [docs](https://docs.cortex.dev) • [examples](https://github.com/cortexlabs/cortex/tree/0.17/examples) • [we're hiring](https://angel.co/cortex-labs-inc/jobs) • [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 +26,7 @@
 
 <!-- CORTEX_VERSION_README_MINOR -->
 ```bash
-$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.17/get-cli.sh)"
+$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.18/get-cli.sh)"
 ```
 
 ### Implement your predictor
@@ -147,6 +140,6 @@ Cortex manages its own Kubernetes cluster so that end-to-end functionality like
 ## Examples
 
 <!-- CORTEX_VERSION_README_MINOR x3 -->
-* [Image classification](https://github.com/cortexlabs/cortex/tree/0.17/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
-* [Search completion](https://github.com/cortexlabs/cortex/tree/0.17/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
-* [Text generation](https://github.com/cortexlabs/cortex/tree/0.17/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.
+* [Image classification](https://github.com/cortexlabs/cortex/tree/0.18/examples/tensorflow/image-classifier): deploy an Inception model to classify images.
+* [Search completion](https://github.com/cortexlabs/cortex/tree/0.18/examples/pytorch/search-completer): deploy Facebook's RoBERTa model to complete search terms.
+* [Text generation](https://github.com/cortexlabs/cortex/tree/0.18/examples/pytorch/text-generator): deploy Hugging Face's DistilGPT2 model to generate text.

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=0.18.0
 
 slim="false"
 while [[ $# -gt 0 ]]; do

build/cli.sh

@@ -19,7 +19,7 @@ set -euo pipefail
 
 ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.18.0
 
 arg1=${1:-""}
 upload="false"

build/lint.sh

@@ -98,7 +98,7 @@ if [ "$is_release_branch" = "true" ]; then
   ! -path "./bin/*" \
   ! -path "./.git/*" \
   ! -name ".*" \
-  -exec grep -R -A 100 -e "CORTEX_VERSION" {} \;)
+  -exec grep -R -A 5 -e "CORTEX_VERSION" {} \;)
   output=$(echo "$output" | grep -e "master" || true)
   if [[ $output ]]; then
     echo 'occurrences of "master" which should be changed to the version number:'

build/push-image.sh

@@ -17,7 +17,7 @@
 
 set -euo pipefail
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.18.0
 
 slim="false"
 while [[ $# -gt 0 ]]; do

docs/cluster-management/config.md

@@ -47,7 +47,7 @@ instance_volume_type: gp2
 
 # whether the subnets used for EC2 instances should be public or private (default: "public")
 # if "public", instances will be assigned public IP addresses; if "private", instances won't have public IPs and a NAT gateway will be created to allow outgoing network requests
-# see https://docs.cortex.dev/v/master/miscellaneous/security#private-cluster for more information
+# see https://docs.cortex.dev/v/0.18/miscellaneous/security#private-cluster for more information
 subnet_visibility: public  # must be "public" or "private"
 
 # whether to include a NAT gateway with the cluster (a NAT gateway is necessary when using private subnets)
@@ -56,12 +56,12 @@ nat_gateway: none  # must be "none", "single", or "highly_available" (highly_ava
 
 # whether the API load balancer should be internet-facing or internal (default: "internet-facing")
 # note: if using "internal", APIs will still be accessible via the public API Gateway endpoint unless you also disable API Gateway in your API's configuration (if you do that, you must configure VPC Peering to connect to your APIs)
-# see https://docs.cortex.dev/v/master/miscellaneous/security#private-cluster for more information
+# see https://docs.cortex.dev/v/0.18/miscellaneous/security#private-cluster for more information
 api_load_balancer_scheme: internet-facing  # must be "internet-facing" or "internal"
 
 # whether the operator load balancer should be internet-facing or internal (default: "internet-facing")
-# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/master/guides/vpc-peering)
-# see https://docs.cortex.dev/v/master/miscellaneous/security#private-cluster for more information
+# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/0.18/guides/vpc-peering)
+# see https://docs.cortex.dev/v/0.18/miscellaneous/security#private-cluster for more information
 operator_load_balancer_scheme: internet-facing  # must be "internet-facing" or "internal"
 
 # CloudWatch log group for cortex (default: <cluster_name>)
@@ -71,10 +71,10 @@ log_group: cortex
 tags:  # <string>: <string> map of key/value pairs
 
 # whether to use spot instances in the cluster (default: false)
-# see https://docs.cortex.dev/v/master/cluster-management/spot-instances for additional details on spot configuration
+# see https://docs.cortex.dev/v/0.18/cluster-management/spot-instances for additional details on spot configuration
 spot: false
 
-# see https://docs.cortex.dev/v/master/guides/custom-domain for instructions on how to set up a custom domain
+# see https://docs.cortex.dev/v/0.18/guides/custom-domain for instructions on how to set up a custom domain
 ssl_certificate_arn:
 ```
 
@@ -85,19 +85,19 @@ The docker images used by the Cortex cluster can also be overridden, although th
 <!-- CORTEX_VERSION_BRANCH_STABLE -->
 ```yaml
 # docker image paths
-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_inferentia: cortexlabs/inferentia:master
-image_neuron_rtd: cortexlabs/neuron-rtd: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_operator: cortexlabs/operator:0.18.0
+image_manager: cortexlabs/manager:0.18.0
+image_downloader: cortexlabs/downloader:0.18.0
+image_request_monitor: cortexlabs/request-monitor:0.18.0
+image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.18.0
+image_metrics_server: cortexlabs/metrics-server:0.18.0
+image_inferentia: cortexlabs/inferentia:0.18.0
+image_neuron_rtd: cortexlabs/neuron-rtd:0.18.0
+image_nvidia: cortexlabs/nvidia:0.18.0
+image_fluentd: cortexlabs/fluentd:0.18.0
+image_statsd: cortexlabs/statsd:0.18.0
+image_istio_proxy: cortexlabs/istio-proxy:0.18.0
+image_istio_pilot: cortexlabs/istio-pilot:0.18.0
+image_istio_citadel: cortexlabs/istio-citadel:0.18.0
+image_istio_galley: cortexlabs/istio-galley:0.18.0
 ```

docs/cluster-management/install.md

@@ -8,7 +8,7 @@
 
 <!-- CORTEX_VERSION_MINOR -->
 ```bash
-$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/master/get-cli.sh)"
+$ bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.18/get-cli.sh)"
 ```
 
 Continue to [deploy an example](#deploy-an-example) below.
@@ -26,7 +26,7 @@ To use GPU nodes, you may need to subscribe to the [EKS-optimized AMI with GPU S
 <!-- 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/0.18/get-cli.sh)"
 
 # provision infrastructure on AWS and spin up a cluster
 $ cortex cluster up
@@ -37,7 +37,7 @@ $ cortex cluster up
 <!-- CORTEX_VERSION_MINOR -->
 ```bash
 # clone the Cortex repository
-$ git clone -b master https://github.com/cortexlabs/cortex.git
+$ git clone -b 0.18 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 configure
 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/0.18/get-cli.sh)"
 
 # confirm version
 cortex version

docs/deployments/deployment.md

@@ -63,4 +63,4 @@ 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](../miscellaneous/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/0.18/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/0.18/examples/pytorch/iris-classifier) exports its trained model like this:
 
 ```python
 torch.save(model.state_dict(), "weights.pth")
@@ -24,7 +24,7 @@ For Inferentia-equipped instances, check the [Inferentia instructions](inferenti
 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/0.18/examples/pytorch/iris-classifier) were to export the model to ONNX, it would look like this:
 
 ```python
 placeholder = torch.randn(1, 4)
@@ -52,7 +52,7 @@ 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/0.18/examples/tensorflow/sentiment-analyzer):
 
 ```python
 import tensorflow as tf
@@ -90,14 +90,14 @@ 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/0.18/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/0.18/examples/tensorflow/license-plate-reader) uses this approach.
 
 ## Scikit-learn
 
@@ -106,7 +106,7 @@ 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/0.18/examples/sklearn/iris-classifier) uses this approach. Here is the relevant code:
 
 ```python
 pickle.dump(model, open("model.pkl", "wb"))
@@ -159,7 +159,7 @@ 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/onnx/iris-classifier](https://github.com/cortexlabs/cortex/blob/master/examples/onnx/iris-classifier) uses this approach. Here is the relevant code:
+[examples/onnx/iris-classifier](https://github.com/cortexlabs/cortex/blob/0.18/examples/onnx/iris-classifier) uses this approach. Here is the relevant code:
 
 ```python
 from onnxmltools.convert import convert_xgboost