Update version comments

docs/cluster-management/aws-credentials.md

@@ -1,7 +1,5 @@
 # AWS credentials
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 As of now, Cortex only runs locally or on AWS. We plan to support other cloud providers in the future. If you don't have an AWS account you can get started with one [here](https://portal.aws.amazon.com/billing/signup#/start).
 
 Follow this [tutorial](https://aws.amazon.com/premiumsupport/knowledge-center/create-access-key) to create an access key. Enable programmatic access for the IAM user, and attach the built-in `AdministratorAccess` policy to your IAM user. If you'd like to use less privileged credentials once the Cortex cluster has been created, see [security](../miscellaneous/security.md).

docs/cluster-management/config.md

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 # Cluster configuration
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 The Cortex cluster may be configured by providing a configuration file to `cortex cluster up` or `cortex cluster configure` via the `--config` flag (e.g. `cortex cluster up --config cluster.yaml`). Below is the schema for the cluster configuration file, with default values shown (unless otherwise specified):
 
 <!-- CORTEX_VERSION_MINOR -->

docs/cluster-management/ec2-instances.md

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 # EC2 instances
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 There are a variety of instance types to choose from when creating a Cortex cluster. If you are unsure about which instance to pick, review these options as a starting point.
 
 This is not a comprehensive guide so please refer to the [AWS's documentation](https://aws.amazon.com/ec2/instance-types/) for more information.

docs/cluster-management/install.md

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 # Install
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## Running on your machine or a single instance
 
 [Docker](https://docs.docker.com/install) is required to run Cortex locally. In addition, your machine (or your Docker Desktop for Mac users) should have at least 8GB of memory if you plan to deploy large deep learning models.

docs/cluster-management/spot-instances.md

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 # Spot instances
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 [Spot instances](https://aws.amazon.com/ec2/spot/) are spare capacity that AWS sells at a discount (up to 90%). The caveat is that spot instances may not always be available, and can be recalled by AWS at anytime. Cortex allows you to use spot instances in your cluster to take advantage of the discount while ensuring uptime and reliability of APIs. You can configure your cluster to use spot instances using the configuration below:
 
 ```yaml

docs/cluster-management/uninstall.md

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 # Uninstall
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## Prerequisites
 
 1. [AWS credentials](aws-credentials.md)

docs/cluster-management/update.md

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 # Update
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## Prerequisites
 
 1. [Docker](https://docs.docker.com/install)

docs/deployments/api-configuration.md

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 # API configuration
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Once your model is [exported](exporting.md) and you've implemented a [Predictor](predictors.md), you can configure your API via a yaml file (typically named `cortex.yaml`).
 
 Reference the section below which corresponds to your Predictor type: [Python](#python-predictor), [TensorFlow](#tensorflow-predictor), or [ONNX](#onnx-predictor).

docs/deployments/autoscaling.md

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 # Autoscaling
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Cortex autoscales your web services on a per-API basis based on your configuration.
 
 ## Replica Parallelism

docs/deployments/compute.md

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 # Compute
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Compute resource requests in Cortex follow the syntax and meaning of [compute resources in Kubernetes](https://kubernetes.io/docs/concepts/configuration/manage-compute-resources-container).
 
 For example:

docs/deployments/deployment.md

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 # API deployment
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Once your model is [exported](exporting.md), you've implemented a [Predictor](predictors.md), and you've [configured your API](api-configuration.md), you're ready to deploy!
 
 ## `cortex deploy`

docs/deployments/exporting.md

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 # Exporting models
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Cortex can deploy models that are exported in a variety of formats. Therefore, it is best practice to export your model by following the recommendations of your machine learning library.
 
 Here are examples for some common ML libraries:

docs/deployments/gpus.md

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 # Using GPUs
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 To use GPUs:
 
 1. Make sure your AWS account is subscribed to the [EKS-optimized AMI with GPU Support](https://aws.amazon.com/marketplace/pp/B07GRHFXGM).

docs/deployments/prediction-monitoring.md

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 # Prediction monitoring
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 You can configure your API to collect prediction metrics and display real-time stats in `cortex get <api_name>`. Cortex looks for scalar values in the response payload. If the response payload is a JSON object, `key` must be used to extract the desired scalar value.
 
 ```yaml

docs/deployments/predictors.md

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 # Predictor implementation
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 Once your model is [exported](exporting.md), you can implement one of Cortex's Predictor classes to deploy your model. A Predictor is a Python class that describes how to initialize your model and use it to make predictions.
 
 Which Predictor you use depends on how your model is exported:

docs/deployments/python-packages.md

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 # Python/Conda packages
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## PyPI packages
 
 You can install your required PyPI packages and import them in your Python files using pip. Cortex looks for a `requirements.txt` file in the top level Cortex project directory (i.e. the directory which contains `cortex.yaml`):

docs/deployments/statuses.md

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 # API statuses
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 | Status                | Meaning |
 | :--- | :--- |
 | live                  | API is deployed and ready to serve prediction requests (at least one replica is running) |

docs/deployments/system-packages.md

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 # System packages
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## Bash script
 
 Cortex looks for a file named `dependencies.sh` in the top level Cortex project directory (i.e. the directory which contains `cortex.yaml`). For example:

docs/guides/api-gateway.md

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 # Set up AWS API Gateway
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 We have plans to automatically configure API gateway when creating a Cortex API ([#326](https://github.com/cortexlabs/cortex/issues/326)), but until that's implemented, it's fairly straightforward to set it up manually.
 
 One reason to use API Gateway is to get HTTPS working with valid certificates (either by using AWS's built-in certificates, or using your own via custom domains and the AWS Certificate Manager). Another reason could be to expose your APIs to the internet when configuring Cortex to use an internal load balancer.

docs/guides/batch-runner.md

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 # Add a batch runner API
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 We have plans to support a batch interface to Cortex APIs ([#523](https://github.com/cortexlabs/cortex/issues/523)), but until that's implemented, it is possible to implement a batch runner which receives the batch request, splits it into individual requests, and sends them to the prediction API.
 
 _Note: this is experimental. Also, this behavior can be implemented outside of Cortex, e.g. in your backend server if you have one._

docs/guides/metrics.md

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 # Plot in-flight requests
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 The `cortex get` and `cortex get API_NAME` commands display the request time (averaged over the past 2 weeks) and response code counts (summed over the past 2 weeks) for your API(s):
 
 ```text

docs/guides/single-node-deployment.md

@@ -1,7 +1,5 @@
 # Single node deployment
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 You can use Cortex to deploy models on a single node. Deploying to a single node can be cheaper than spinning up a Cortex cluster with 1 worker node. It also may be useful for testing your model on a GPU if you don't have access to one locally.
 
 Deploying on a single node entails `ssh`ing into that instance and running Cortex locally. When using this approach, you won't get the the advantages of deploying to a cluster such as autoscaling, rolling updates, etc.

docs/guides/ssh-instance.md

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 # SSH into worker instance
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 There are some cases when SSH-ing into an AWS Cortex instance may be necessary.
 
 This can be done via the AWS web UI or via the terminal. The first 5 steps are identical for both approaches.

docs/guides/subdomain-https-setup.md

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 # Set up HTTPS on a subdomain
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 The recommended way to set up HTTPS with trusted certificates is by using [API Gateway](../api-gateway.md) because it's simpler and enables you to use API Gateway features such as rate limiting (it also supports custom domains). This guide is only recommended if HTTPS is required and you don't wish to use API Gateway (e.g. it doesn't support your use case due to limitations such as the 29 second request timeout).
 
 This guide will demonstrate how to create a dedicated subdomain in AWS Route 53 and use an SSL certificate provisioned by AWS Certificate Manager (ACM) to support HTTPS traffic to Cortex APIs. By the end of this guide, you will have a Cortex cluster with APIs accessible via `https://<your-subdomain>/<api-endpoint>`.

docs/guides/vpc-peering.md

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 # Set up VPC peering
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 If you are using an internal operator load balancer (i.e. you set `operator_load_balancer_scheme: internal` in your cluster configuration file before creating your cluster), you can use VPC Peering to enable your Cortex CLI to connect to your cluster operator from another VPC so that you may run `cortex` commands.
 
 If you are using an internal API load balancer (i.e. you set `api_load_balancer_scheme: internal` in your cluster configuration file before creating your cluster), you can use VPC Peering to enable prediction requests from another VPC. _Note: if you intend to create a public endpoint for your internal API load balancer, see our [API Gateway guide](api-gateway.md)._

docs/miscellaneous/architecture.md

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 # Architecture diagram
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ![architecture diagram](https://user-images.githubusercontent.com/808475/81362760-7293bb80-9096-11ea-92e3-475c673b3dbc.png)
 
 _note: this diagram is simplified for illustrative purposes_

docs/miscellaneous/cli.md

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 # CLI commands
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 ## deploy
 
 ```text

docs/miscellaneous/environments.md

@@ -1,7 +1,5 @@
 # Environments
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 The `cortex` CLI can be used to deploy models locally and/or to any number of clusters. Environments are used to select which cluster to use for a `cortex` command. An environment contains the information required to connect to a cluster (e.g. AWS credentials and Cortex operator URL).
 
 By default, the CLI ships with a single environment named `local`. This is the default environment for all Cortex commands (other than `cortex cluster` commands), which means that APIs will be deployed locally by default.

docs/miscellaneous/security.md

@@ -1,7 +1,5 @@
 # Security
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 _The information on this page assumes you are running Cortex on AWS. If you're only deploying locally, this information does not apply (although AWS credentials can still be passed into your APIs, and can be specified with `cortex env configure local`)_
 
 ## Private cluster

docs/miscellaneous/telemetry.md

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 # Telemetry
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 By default, Cortex sends anonymous usage data to Cortex Labs.
 
 ## What data is collected?

docs/troubleshooting/nvidia-container-runtime-not-found.md

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 # NVIDIA container runtime not found
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 When attempting to deploy a model to a GPU in the local environment, you may encounter NVIDIA container runtime not found. Since Cortex uses Docker to deploy APIs in the local environment, your Docker engine must have the NVIDIA container runtime installed (the NVIDIA container runtime is responsible for exposing your GPU to the Docker engine).
 
 ## Check Compatibility

docs/troubleshooting/stuck-updating.md

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 # API is stuck updating
 
-_WARNING: you are on the master branch, please refer to the docs on the branch that matches your `cortex version`_
-
 There are a few possible causes for APIs getting stuck in the "updating" or "compute unavailable" state. Here are some things to check:
 
 ## Check `cortex logs API_NAME`

examples/keras/document-denoiser/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: document-denoiser
   predictor:

examples/keras/document-denoiser/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import boto3, base64, cv2, re, os, requests
 from botocore import UNSIGNED

examples/keras/document-denoiser/trainer.ipynb

@@ -13,7 +13,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# _WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`_\n",
+    "# _this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex_\n",
     "\n",
     "\n",
     "import keras\n",

examples/pytorch/answer-generator/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: answer-generator
   predictor:

examples/pytorch/answer-generator/generator.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 # This file includes code which was modified from https://colab.research.google.com/drive/1KTLqiAOdKM_3RnBWfqgrvOQLqumUyOdA
 

examples/pytorch/answer-generator/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import wget
 import torch

examples/pytorch/image-classifier/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: image-classifier
   predictor:

examples/pytorch/image-classifier/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import requests
 import torch

examples/pytorch/iris-classifier/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: iris-classifier
   predictor:

examples/pytorch/iris-classifier/model.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import torch
 import torch.nn as nn

examples/pytorch/iris-classifier/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import re
 import torch

examples/pytorch/language-identifier/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: language-identifier
   predictor:

examples/pytorch/language-identifier/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import wget
 import fasttext

examples/pytorch/object-detector/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: object-detector
   predictor:

examples/pytorch/object-detector/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 from io import BytesIO
 

examples/pytorch/reading-comprehender/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: reading-comprehender
   predictor:

examples/pytorch/reading-comprehender/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import torch
 from allennlp.predictors.predictor import Predictor as AllenNLPPredictor

examples/pytorch/search-completer/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: search-completer
   predictor:

examples/pytorch/search-completer/predictor.py

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 import torch
 import regex

examples/pytorch/sentiment-analyzer/cortex.yaml

@@ -1,4 +1,4 @@
-# WARNING: you are on the master branch, please refer to the examples on the branch that matches your `cortex version`
+# this is an example for cortex release 0.17 and may not deploy correctly on other releases of cortex
 
 - name: sentiment-analyzer
   predictor: