Update version to 0.21.0

Author: vishalbollu

README.md

@@ -1,15 +1,3 @@
-<!-- Delete on release branches -->
-<img src='https://s3-us-west-2.amazonaws.com/cortex-public/logo.png' height='42'>
-
-<br>
-
-<!-- Delete on release branches -->
-<!-- CORTEX_VERSION_README_MINOR -->
-
-[install](https://docs.cortex.dev/install) • [documentation](https://docs.cortex.dev) • [examples](https://github.com/cortexlabs/cortex/tree/0.20/examples) • [we're hiring](https://angel.co/cortex-labs-inc/jobs) • [chat with us](https://gitter.im/cortexlabs/cortex)
-
-<br>
-
 # Model serving at scale
 
 ### Deploy
@@ -142,10 +130,9 @@ cortex is ready!
 
 ## Get started
 
-<!-- CORTEX_VERSION_README_MINOR -->
 ```bash
-bash -c "$(curl -sS https://raw.githubusercontent.com/cortexlabs/cortex/0.20/get-cli.sh)"
+pip install cortex
 ```
 
 <!-- CORTEX_VERSION_README_MINOR -->
-See our [installation guide](https://docs.cortex.dev/install), then deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.20/examples) or bring your own models to build [realtime APIs](https://docs.cortex.dev/deployments/realtime-api) and [batch APIs](https://docs.cortex.dev/deployments/batch-api).
+See our [installation guide](https://docs.cortex.dev/install), then deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.21/examples) or bring your own models to build [realtime APIs](https://docs.cortex.dev/deployments/realtime-api) and [batch APIs](https://docs.cortex.dev/deployments/batch-api).

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=0.21.0
 
 image=$1
 dir="${ROOT}/images/${image/-slim}"

build/cli.sh

@@ -19,7 +19,7 @@ set -euo pipefail
 
 ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.21.0
 
 arg1=${1:-""}
 upload="false"

build/push-image.sh

@@ -17,7 +17,7 @@
 
 set -euo pipefail
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.21.0
 
 image=$1
 

cli/cluster/errors.go

@@ -55,7 +55,7 @@ func ErrorFailedToConnectOperator(originalError error, envName string, operatorU
 	msg += "\nif you have a cluster running:\n"
 	msg += fmt.Sprintf("    → run `cortex cluster info --env %s` to update your environment (include `--config <cluster.yaml>` if you have a cluster configuration file)\n", envName)
 	// CORTEX_VERSION_MINOR
-	msg += "    → if you set `operator_load_balancer_scheme: internal` in your cluster configuration file, your CLI must run from within a VPC that has access to your cluster's VPC (see https://docs.cortex.dev/v/master/guides/vpc-peering)\n"
+	msg += "    → if you set `operator_load_balancer_scheme: internal` in your cluster configuration file, your CLI must run from within a VPC that has access to your cluster's VPC (see https://docs.cortex.dev/v/0.21/guides/vpc-peering)\n"
 
 	return errors.WithStack(&errors.Error{
 		Kind:    ErrFailedToConnectOperator,

docs/cluster-management/config.md

@@ -39,7 +39,7 @@ 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/0.21/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)
@@ -48,12 +48,12 @@ 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/0.21/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-operator 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/0.21/guides/vpc-peering)
+# see https://docs.cortex.dev/v/0.21/miscellaneous/security#private-operator for more information
 operator_load_balancer_scheme: internet-facing  # must be "internet-facing" or "internal"
 
 # whether to disable API gateway cluster-wide
@@ -65,10 +65,10 @@ api_gateway: public  # must be "public" or "none"
 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/0.21/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/0.21/guides/custom-domain for instructions on how to set up a custom domain
 ssl_certificate_arn:
 
 # primary CIDR block for the cluster's VPC (default: 192.168.0.0/16)
@@ -82,17 +82,17 @@ 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_operator: cortexlabs/operator:0.21.0
+image_manager: cortexlabs/manager:0.21.0
+image_downloader: cortexlabs/downloader:0.21.0
+image_request_monitor: cortexlabs/request-monitor:0.21.0
+image_cluster_autoscaler: cortexlabs/cluster-autoscaler:0.21.0
+image_metrics_server: cortexlabs/metrics-server:0.21.0
+image_inferentia: cortexlabs/inferentia:0.21.0
+image_neuron_rtd: cortexlabs/neuron-rtd:0.21.0
+image_nvidia: cortexlabs/nvidia:0.21.0
+image_fluentd: cortexlabs/fluentd:0.21.0
+image_statsd: cortexlabs/statsd:0.21.0
+image_istio_proxy: cortexlabs/istio-proxy:0.21.0
+image_istio_pilot: cortexlabs/istio-pilot:0.21.0
 ```

docs/cluster-management/install.md

@@ -16,7 +16,7 @@ See [here](../miscellaneous/cli.md#install-cortex-cli-without-python-client) to
 <!-- CORTEX_VERSION_MINOR -->
 ```bash
 # clone the Cortex repository
-git clone -b master https://github.com/cortexlabs/cortex.git
+git clone -b 0.21 https://github.com/cortexlabs/cortex.git
 
 # navigate to the Pytorch text generator example
 cd cortex/examples/pytorch/text-generator
@@ -87,6 +87,6 @@ You can now run the same commands shown above to deploy the text generator to AW
 
 <!-- CORTEX_VERSION_MINOR -->
 * Try the [tutorial](../../examples/pytorch/text-generator/README.md) to learn more about how to use Cortex.
-* Deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/master/examples).
+* Deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.21/examples).
 * See our [exporting guide](../guides/exporting.md) for how to export your model to use in an API.
 * See [uninstall](uninstall.md) if you'd like to spin down your cluster.

docs/cluster-management/update.md

@@ -17,7 +17,7 @@ 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/0.21/get-cli.sh)"
 
 # confirm version
 cortex version

docs/deployments/batch-api/deployment.md

@@ -122,4 +122,4 @@ deleting my-api
 <!-- CORTEX_VERSION_MINOR -->
 * [Tutorial](../../../examples/batch/image-classifier/README.md) provides a step-by-step walkthrough of deploying an image classification batch API
 * [CLI documentation](../../miscellaneous/cli.md) lists all CLI commands
-* [Examples](https://github.com/cortexlabs/cortex/tree/master/examples/batch) demonstrate how to deploy models from common ML libraries
+* [Examples](https://github.com/cortexlabs/cortex/tree/0.21/examples/batch) demonstrate how to deploy models from common ML libraries

docs/deployments/batch-api/predictors.md

@@ -95,7 +95,7 @@ For proper separation of concerns, it is recommended to use the constructor's `c
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-You can find an example of a BatchAPI using a PythonPredictor in [examples/batch/image-classifier](https://github.com/cortexlabs/cortex/tree/master/examples/batch/image-classifier).
+You can find an example of a BatchAPI using a PythonPredictor in [examples/batch/image-classifier](https://github.com/cortexlabs/cortex/tree/0.21/examples/batch/image-classifier).
 
 ### Pre-installed packages
 
@@ -166,7 +166,7 @@ torchvision==0.6.1
 ```
 
 <!-- CORTEX_VERSION_MINOR x3 -->
-The pre-installed system packages are listed in [images/python-predictor-cpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-predictor-cpu/Dockerfile) (for CPU), [images/python-predictor-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-predictor-gpu/Dockerfile) (for GPU), or [images/python-predictor-inf/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/python-predictor-inf/Dockerfile) (for Inferentia).
+The pre-installed system packages are listed in [images/python-predictor-cpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/python-predictor-cpu/Dockerfile) (for CPU), [images/python-predictor-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/python-predictor-gpu/Dockerfile) (for GPU), or [images/python-predictor-inf/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/python-predictor-inf/Dockerfile) (for Inferentia).
 
 If your application requires additional dependencies, you can install additional [Python packages](../python-packages.md) and [system packages](../system-packages.md).
 
@@ -223,7 +223,7 @@ 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/0.21/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.
 
 When multiple models are defined using the Predictor's `models` field, the `tensorflow_client.predict()` method expects a second argument `model_name` which must hold the name of the model that you want to use for inference (for example: `self.client.predict(payload, "text-generator")`). See the [multi model guide](../../guides/multi-model.md#tensorflow-predictor) for more information.
 
@@ -232,7 +232,7 @@ For proper separation of concerns, it is recommended to use the constructor's `c
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-You can find an example of a BatchAPI using a TensorFlowPredictor in [examples/batch/tensorflow](https://github.com/cortexlabs/cortex/tree/master/examples/batch/tensorflow).
+You can find an example of a BatchAPI using a TensorFlowPredictor in [examples/batch/tensorflow](https://github.com/cortexlabs/cortex/tree/0.21/examples/batch/tensorflow).
 
 ### Pre-installed packages
 
@@ -253,7 +253,7 @@ tensorflow==2.3.0
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-The pre-installed system packages are listed in [images/tensorflow-predictor/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/tensorflow-predictor/Dockerfile).
+The pre-installed system packages are listed in [images/tensorflow-predictor/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/tensorflow-predictor/Dockerfile).
 
 If your application requires additional dependencies, you can install additional [Python packages](../python-packages.md) and [system packages](../system-packages.md).
 
@@ -310,7 +310,7 @@ 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/0.21/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.
 
 When multiple models are defined using the Predictor's `models` field, the `onnx_client.predict()` method expects a second argument `model_name` which must hold the name of the model that you want to use for inference (for example: `self.client.predict(model_input, "text-generator")`). See the [multi model guide](../../guides/multi-model.md#onnx-predictor) for more information.
 
@@ -319,7 +319,7 @@ For proper separation of concerns, it is recommended to use the constructor's `c
 ### Examples
 
 <!-- CORTEX_VERSION_MINOR -->
-You can find an example of a BatchAPI using an ONNXPredictor in [examples/batch/onnx](https://github.com/cortexlabs/cortex/tree/master/examples/batch/onnx).
+You can find an example of a BatchAPI using an ONNXPredictor in [examples/batch/onnx](https://github.com/cortexlabs/cortex/tree/0.21/examples/batch/onnx).
 
 ### Pre-installed packages
 
@@ -337,6 +337,6 @@ requests==2.24.0
 ```
 
 <!-- CORTEX_VERSION_MINOR x2 -->
-The pre-installed system packages are listed in [images/onnx-predictor-cpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/onnx-predictor-cpu/Dockerfile) (for CPU) or [images/onnx-predictor-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/master/images/onnx-predictor-gpu/Dockerfile) (for GPU).
+The pre-installed system packages are listed in [images/onnx-predictor-cpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/onnx-predictor-cpu/Dockerfile) (for CPU) or [images/onnx-predictor-gpu/Dockerfile](https://github.com/cortexlabs/cortex/tree/0.21/images/onnx-predictor-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).