doc cleanup

ci/buildspec.yml

@@ -7,7 +7,7 @@ phases:
     commands:
       - apt-get update
       - apt-get install sudo -y
-      - python ts_scripts/install_dependencies.py --cuda=cu101 --environment=dev
+      - python ts_scripts/install_dependencies.py --cuda=cu102 --environment=dev
 
   build:
     commands:

docker/README.md

@@ -32,8 +32,10 @@ Use `build_image.sh` script to build the docker images. The script builds the `p
 |-b, --branch_name|Specify a branch name to use. Default: master |
 |-g, --gpu|Build image with GPU based ubuntu base image|
 |-bt, --buildtype|Which type of docker image to build. Can be one of : production, dev, codebuild|
-|-t, --tag|Tag name for image. If not specified, script uses torchserv default tag names.|
+|-t, --tag|Tag name for image. If not specified, script uses torchserve default tag names.|
 |-cv, --cudaversion| Specify to cuda version to use. Supported values `cu92`, `cu101`, `cu102`, `cu111`. Default `cu102`|
+|--codebuild| Set if you need [AWS CodeBuild](https://aws.amazon.com/codebuild/)|
+
 
 **PRODUCTION ENVIRONMENT IMAGES**
 

docs/index.md

@@ -18,6 +18,7 @@ TorchServe is a flexible and easy to use tool for serving PyTorch models.
    * [Metrics API](metrics_api.md) - How to configure metrics API
 * [Batch inference with TorchServe](batch_inference_with_ts.md) - How to create and serve a model with batch inference in TorchServe
 * [Model Zoo](model_zoo.md) - List of pre-trained model archives ready to be served for inference with TorchServe.
+* [Examples](https://github.com/pytorch/serve/tree/master/examples) - Many examples of how to package and deploy models and workflows with TorchServe
 
 ## Advanced Features