Update version to 0.23.0

Author: deliahu

README.md

@@ -1,13 +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/0.22/examples) • [support](https://gitter.im/cortexlabs/cortex)
-
 # Deploy machine learning models to production
 
 Cortex is an open source platform for deploying, managing, and scaling machine learning in production.
@@ -16,53 +6,47 @@ Cortex is an open source platform for deploying, managing, and scaling machine l
 
 ## Model serving infrastructure
 
-* Supports deploying TensorFlow, PyTorch, sklearn and other models as realtime or batch APIs
-* Ensures high availability with availability zones and automated instance restarts
-* Scales to handle production workloads with request-based autoscaling
-* Runs inference on spot instances with on-demand backups
-* Manages traffic splitting for A/B testing
+* Supports deploying TensorFlow, PyTorch, sklearn and other models as realtime or batch APIs.
+* Ensures high availability with availability zones and automated instance restarts.
+* Runs inference on spot instances with on-demand backups.
+* Autoscales to handle production workloads.
 
-#### Configure your cluster:
+#### Configure Cortex
 
 ```yaml
 # cluster.yaml
 
 region: us-east-1
-availability_zones: [us-east-1a, us-east-1b]
-api_gateway: public
 instance_type: g4dn.xlarge
+spot: true
 min_instances: 10
 max_instances: 100
-spot: true
 ```
 
-#### Spin up your cluster on your AWS account:
+#### Spin up Cortex on your AWS account
 
 ```text
 $ cortex cluster up --config cluster.yaml
 
 ￮ configuring autoscaling ✓
 ￮ configuring networking ✓
 ￮ configuring logging ✓
-￮ configuring metrics dashboard ✓
 
 cortex is ready!
 ```
 
 <br>
 
-## Reproducible model deployments
+## Reproducible deployments
 
-* Implement request handling in Python
-* Customize compute, autoscaling, and networking for each API
-* Package dependencies, code, and configuration for reproducible deployments
-* Test locally before deploying to your cluster
+* Package dependencies, code, and configuration for reproducible deployments.
+* Configure compute, autoscaling, and networking for each API.
+* Integrate with your data science platform or CI/CD system.
+* Test locally before deploying to your cluster.
 
-#### Implement a predictor:
+#### Implement a predictor
 
 ```python
-# predictor.py
-
 from transformers import pipeline
 
 class PythonPredictor:
@@ -73,70 +57,63 @@ class PythonPredictor:
     return self.model(payload["text"])[0]
 ```
 
-#### Configure an API:
-
-```yaml
-# cortex.yaml
-
-name: text-generator
-kind: RealtimeAPI
-predictor:
-  path: predictor.py
-compute:
-  gpu: 1
-  mem: 4Gi
-autoscaling:
-  min_replicas: 1
-  max_replicas: 10
-networking:
-  api_gateway: public
-```
-
-#### Deploy to production:
-
-```text
-$ cortex deploy cortex.yaml
-
-creating https://example.com/text-generator
-
-$ curl https://example.com/text-generator \
-    -X POST -H "Content-Type: application/json" \
-    -d '{"text": "deploy machine learning models to"}'
+#### Configure an API
 
-"deploy machine learning models to production"
+```python
+api_spec = {
+  "name": "text-generator",
+  "kind": "RealtimeAPI",
+  "compute": {
+    "gpu": 1,
+    "mem": "8Gi",
+  },
+  "autoscaling": {
+    "min_replicas": 1,
+    "max_replicas": 10
+  },
+  "networking": {
+    "api_gateway": "public"
+  }
+}
 ```
 
 <br>
 
-## API management
+## Scalable machine learning APIs
 
-* Monitor API performance
-* Aggregate and stream logs
-* Customize prediction tracking
-* Update APIs without downtime
+* Scale to handle production workloads with request-based autoscaling.
+* Stream performance metrics and logs to any monitoring tool.
+* Serve many models efficiently with multi model caching.
+* Configure traffic splitting for A/B testing.
+* Update APIs without downtime.
 
-#### Manage your APIs:
+#### Deploy to your cluster
 
-```text
-$ cortex get
+```python
+import cortex
 
-realtime api       status     replicas   last update   latency   requests
+cx = cortex.client()
+cx.deploy(api_spec, predictor=PythonPredictor)
 
-text-generator     live       34         9h            247ms     71828
-object-detector    live       13         15h           23ms      828459
+# creating https://example.com/text-generator
+```
 
+#### Consume your API
 
-batch api          running jobs   last update
+```python
+import requests
 
-image-classifier   5              10h
+endpoint = "https://example.com/text-generator"
+payload = {"text": "hello world"}
+prediction = requests.post(endpoint, payload)
 ```
 
 <br>
 
 ## Get started
 
-```text
-$ pip install cortex
+```bash
+pip install cortex
 ```
 
 See the [installation guide](https://docs.cortex.dev/install) for next steps.

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=0.23.0
 REGISTRY_URL=quay.io
 
 image=$1

build/cli.sh

@@ -19,7 +19,7 @@ set -euo pipefail
 
 ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")"/.. >/dev/null && pwd)"
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.23.0
 
 arg1=${1:-""}
 upload="false"

build/push-image.sh

@@ -17,7 +17,7 @@
 
 set -euo pipefail
 
-CORTEX_VERSION=master
+CORTEX_VERSION=0.23.0
 REGISTRY_URL=quay.io
 
 image=$1

docs/aws/install.md

@@ -18,7 +18,7 @@ cortex env default aws
 ```
 
 <!-- CORTEX_VERSION_MINOR -->
-Try the [tutorial](../../examples/pytorch/text-generator/README.md) or deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/master/examples).
+Try the [tutorial](../../examples/pytorch/text-generator/README.md) or deploy one of our [examples](https://github.com/cortexlabs/cortex/tree/0.23/examples).
 
 ## Configure Cortex
 
@@ -63,7 +63,7 @@ nat_gateway: none
 api_load_balancer_scheme: internet-facing
 
 # operator load balancer scheme [internet-facing | internal]
-# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/master/aws/vpc-peering)
+# note: if using "internal", you must configure VPC Peering to connect your CLI to your cluster operator (https://docs.cortex.dev/v/0.23/aws/vpc-peering)
 operator_load_balancer_scheme: internet-facing
 
 # API Gateway [public (API Gateway will be used by default, can be disabled per API) | none (API Gateway will be disabled for all APIs)]
@@ -86,19 +86,19 @@ The docker images used by the Cortex cluster can also be overridden, although th
 
 <!-- CORTEX_VERSION_BRANCH_STABLE -->
 ```yaml
-image_operator: quay.io/cortexlabs/operator:master
-image_manager: quay.io/cortexlabs/manager:master
-image_downloader: quay.io/cortexlabs/downloader:master
-image_request_monitor: quay.io/cortexlabs/request-monitor:master
-image_cluster_autoscaler: quay.io/cortexlabs/cluster-autoscaler:master
-image_metrics_server: quay.io/cortexlabs/metrics-server:master
-image_inferentia: quay.io/cortexlabs/inferentia:master
-image_neuron_rtd: quay.io/cortexlabs/neuron-rtd:master
-image_nvidia: quay.io/cortexlabs/nvidia:master
-image_fluentd: quay.io/cortexlabs/fluentd:master
-image_statsd: quay.io/cortexlabs/statsd:master
-image_istio_proxy: quay.io/cortexlabs/istio-proxy:master
-image_istio_pilot: quay.io/cortexlabs/istio-pilot:master
+image_operator: quay.io/cortexlabs/operator:0.23.0
+image_manager: quay.io/cortexlabs/manager:0.23.0
+image_downloader: quay.io/cortexlabs/downloader:0.23.0
+image_request_monitor: quay.io/cortexlabs/request-monitor:0.23.0
+image_cluster_autoscaler: quay.io/cortexlabs/cluster-autoscaler:0.23.0
+image_metrics_server: quay.io/cortexlabs/metrics-server:0.23.0
+image_inferentia: quay.io/cortexlabs/inferentia:0.23.0
+image_neuron_rtd: quay.io/cortexlabs/neuron-rtd:0.23.0
+image_nvidia: quay.io/cortexlabs/nvidia:0.23.0
+image_fluentd: quay.io/cortexlabs/fluentd:0.23.0
+image_statsd: quay.io/cortexlabs/statsd:0.23.0
+image_istio_proxy: quay.io/cortexlabs/istio-proxy:0.23.0
+image_istio_pilot: quay.io/cortexlabs/istio-pilot:0.23.0
 ```
 
 The default docker images used for your Predictors are listed in the instructions for [system packages](../deployments/system-packages.md), and can be overridden in your [Realtime API configuration](../deployments/realtime-api/api-configuration.md) and in your [Batch API configuration](../deployments/batch-api/api-configuration.md).

docs/aws/update.md

@@ -15,7 +15,7 @@ cortex cluster configure  # or: cortex cluster configure --config cluster.yaml
 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/0.23/get-cli.sh)"
 
 # confirm version
 cortex version

docs/deployments/batch-api/api-configuration.md

@@ -15,7 +15,7 @@ Reference the section below which corresponds to your Predictor type: [Python](#
     path: <string>  # path to a python file with a PythonPredictor class definition, relative to the Cortex root (required)
     config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
     python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
-    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/python-predictor-cpu:master or quay.io/cortexlabs/python-predictor-gpu:master based on compute)
+    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/python-predictor-cpu:0.23.0 or quay.io/cortexlabs/python-predictor-gpu:0.23.0 based on compute)
     env: <string: string>  # dictionary of environment variables
   networking:
     endpoint: <string>  # the endpoint for the API (default: <api_name>)
@@ -50,8 +50,8 @@ See additional documentation for [compute](../compute.md), [networking](../../aw
       batch_interval: <duration>  # the maximum amount of time to spend waiting for additional requests before running inference on the batch of requests
     config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
     python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
-    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/tensorflow-predictor:master)
-    tensorflow_serving_image: <string> # docker image to use for the TensorFlow Serving container (default: quay.io/cortexlabs/tensorflow-serving-gpu:master or quay.io/cortexlabs/tensorflow-serving-cpu:master based on compute)
+    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/tensorflow-predictor:0.23.0)
+    tensorflow_serving_image: <string> # docker image to use for the TensorFlow Serving container (default: quay.io/cortexlabs/tensorflow-serving-gpu:0.23.0 or quay.io/cortexlabs/tensorflow-serving-cpu:0.23.0 based on compute)
     env: <string: string>  # dictionary of environment variables
   networking:
     endpoint: <string>  # the endpoint for the API (default: <api_name>)
@@ -82,7 +82,7 @@ See additional documentation for [compute](../compute.md), [networking](../../aw
       ...
     config: <string: value>  # arbitrary dictionary passed to the constructor of the Predictor (can be overridden by config passed in job submission) (optional)
     python_path: <string>  # path to the root of your Python folder that will be appended to PYTHONPATH (default: folder containing cortex.yaml)
-    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/onnx-predictor-gpu:master or quay.io/cortexlabs/onnx-predictor-cpu:master based on compute)
+    image: <string> # docker image to use for the Predictor (default: quay.io/cortexlabs/onnx-predictor-gpu:0.23.0 or quay.io/cortexlabs/onnx-predictor-cpu:0.23.0 based on compute)
     env: <string: string>  # dictionary of environment variables
   networking:
     endpoint: <string>  # the endpoint for the API (default: <api_name>)

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/0.23/examples/batch) demonstrate how to deploy models from common ML libraries