Colab links (#486)

examples/image-classifier/README.md

@@ -4,6 +4,8 @@ This example shows how to deploy an Image Classifier made with Pytorch. The Pyto
 
 ## Define a deployment
 
+A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the model from the `cortex-examples` S3 bucket, preprocess the request payload and postprocess the model inference with the functions defined in `alexnet_handler.py`.
+
 ```yaml
 - kind: deployment
   name: image-classifier
@@ -13,8 +15,11 @@ This example shows how to deploy an Image Classifier made with Pytorch. The Pyto
   model: s3://cortex-examples/image-classifier/alexnet.onnx
   request_handler: alexnet_handler.py
 ```
+<!-- CORTEX_VERSION_MINOR x2 -->
+You can run the code that generated the exported models used in this example folder here:
+- [Pytorch Alexnet](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/image-classifier/alexnet.ipynb)
+- [Tensorflow Inception V3](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/image-classifier/inception.ipynb)
 
-A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the model from the `cortex-examples` S3 bucket, preprocess the request payload and postprocess the model inference with the functions defined in `alexnet_handler.py`.
 
 ## Add request handling
 

examples/iris-classifier/README.md

@@ -15,6 +15,13 @@ Define a `deployment` and an `api` resource in `cortex.yaml`. A `deployment` spe
   model: s3://cortex-examples/iris-classifier/tensorflow
   request_handler: handlers/tensorflow.py
 ```
+<!-- CORTEX_VERSION_MINOR x5 -->
+You can run the code that generated the exported models used in this folder example here:
+- [Tensorflow](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/iris-classifier/models/tensorflow.ipynb)
+- [Pytorch](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/iris-classifier/models/pytorch.ipynb)
+- [Keras](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/iris-classifier/models/keras.ipynb)
+- [XGBoost](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/iris-classifier/models/xgboost.ipynb)
+- [sklearn](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/iris-classifier/models/sklearn.ipynb)
 
 ## Add request handling
 

examples/sentiment-analysis/README.md

@@ -4,6 +4,8 @@ This example shows how to deploy a sentiment analysis classifier trained using [
 
 ## Define a deployment
 
+A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the model from the `cortex-examples` S3 bucket and preprocess the payload and postprocess the inference with functions defined in `sentiment.py`.
+
 ```yaml
 - kind: deployment
   name: sentiment
@@ -13,8 +15,9 @@ This example shows how to deploy a sentiment analysis classifier trained using [
   model: s3://cortex-examples/sentiment-analysis/bert
   request_handler: sentiment.py
 ```
+<!-- CORTEX_VERSION_MINOR -->
+You can run the code that generated the exported BERT model [here](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/sentiment-analysis/bert.ipynb).
 
-A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the model from the `cortex-examples` S3 bucket and preprocess the payload and postprocess the inference with functions defined in `sentiment.py`.
 
 ## Add request handling
 

examples/text-generator/README.md

@@ -4,6 +4,8 @@ This example shows how to deploy OpenAI's GPT-2 model as a service on AWS.
 
 ## Define a deployment
 
+A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the 124M GPT-2 model from the `cortex-examples` S3 bucket, preprocess the payload and postprocess the inference with functions defined in `encoder.py` and deploy each replica of the API on 1 GPU.
+
 ```yaml
 - kind: deployment
   name: text
@@ -15,8 +17,8 @@ This example shows how to deploy OpenAI's GPT-2 model as a service on AWS.
   compute:
     gpu: 1
 ```
-
-A `deployment` specifies a set of resources that are deployed as a single unit. An `api` makes a model available as a web service that can serve real-time predictions. This configuration will download the 124M GPT-2 model from the `cortex-examples` S3 bucket, preprocess the payload and postprocess the inference with functions defined in `encoder.py` and deploy each replica of the API on 1 GPU.
+<!-- CORTEX_VERSION_MINOR -->
+You can run the code that generated the exported GPT-2 model [here](https://colab.research.google.com/github/cortexlabs/cortex/blob/master/examples/text-generator/gpt-2.ipynb).
 
 ## Add request handling