Fixing FAQs doc per issue #2204 (#2351)

* Update index.md

Update to fix a broken link in index.md where the trailing .md is cut off from the management_api.md. Added an anchor link to force the .md to show up.

* Update to index.md

Update to index.md to fix several links ending in .md that sphinx is breaking. Added anchor links to each link and a corresponding anchor in the affected doc. Tested locally and seems to be working.

* Update inference_api.md

* Updated typos

Fixed typos and updated wordslist.txt

* Update wordlist.txt

* FAQs Updates

Updated a couple of broken links on the FAQ site per issue #2204

* updates to resolve links

* Update some links in index

Updated some links in index.md to go to the pytorch html page instead of github. This is a nicer fix for the .md sphinx issue

---------

Co-authored-by: sekyonda <7411+sekyonda@users.noreply.ghe.oculus-rep.com>
Co-authored-by: lxning <23464292+lxning@users.noreply.github.com>
Co-authored-by: Ankith Gunapal <agunapal@ischool.Berkeley.edu>

Author: 4 people

docs/FAQs.md

@@ -15,8 +15,8 @@ Torchserve API's are compliant with the [OpenAPI specification 3.0](https://swag
 
 ### How to use Torchserve in production?
 Depending on your use case, you will be able to deploy torchserve in production using following mechanisms.
-> Standalone deployment. Refer [TorchServe docker documentation](https://github.com/pytorch/serve/tree/master/docker#readme) or [TorchServe documentation](https://github.com/pytorch/serve/tree/master/docs#readme)
-> Cloud based deployment. Refer [TorchServe kubernetes documentation](https://github.com/pytorch/serve/tree/master/kubernetes#readme) or [TorchServe cloudformation documentation](https://github.com/pytorch/serve/tree/master/examples/cloudformation/README.md)
+> Standalone deployment. Refer [TorchServe docker documentation](https://github.com/pytorch/serve/tree/master/docker#readme) or [TorchServe documentation](README.md)
+> Cloud based deployment. Refer [TorchServe kubernetes documentation](https://github.com/pytorch/serve/tree/master/kubernetes#readme) or [TorchServe cloudformation documentation](https://github.com/pytorch/serve/tree/master/examples/cloudformation/README.md#cloudformation)
 
 
 ### What's difference between Torchserve and a python web app using web frameworks like Flask, Django?
@@ -25,7 +25,7 @@ Torchserve's main purpose is to serve models via http REST APIs, Torchserve is n
 Relevant issues: [[581](https://github.com/pytorch/serve/issues/581),[569](https://github.com/pytorch/serve/issues/569)]
 
 ### Are there any sample Models available?
-Various models are provided in Torchserve out of the box. Checkout out Torchserve [Model Zoo](https://github.com/pytorch/serve/blob/master/docs/model_zoo.md) for list of all available models. You can also check out the [examples](https://github.com/pytorch/serve/tree/master/examples) folder.
+Various models are provided in Torchserve out of the box. Checkout out Torchserve [Model Zoo](model_zoo.md) for list of all available models. You can also check out the [examples](https://github.com/pytorch/serve/tree/master/examples) folder.
 
 ### Does Torchserve support other models based on programming languages other than python?
 No, As of now only python based models are supported.
@@ -40,39 +40,39 @@ If a model converts international language string to bytes, client needs to use
 
 ## Deployment and config
 Relevant documents.
-- [Torchserve configuration](https://github.com/pytorch/serve/blob/master/docs/configuration.md)
-- [Model zoo](https://github.com/pytorch/serve/blob/master/docs/model_zoo.md#model-zoo)
-- [Snapshot](https://github.com/pytorch/serve/blob/master/docs/snapshot.md)
-- [Docker](../docker/README.md)
+- [Torchserve configuration](configuration.md)
+- [Model zoo](model_zoo.md)
+- [Snapshot](snapshot.md)
+- [Docker](https://github.com/pytorch/serve/blob/master/docker/README.md#docker-readme)
 
 ### Can I run Torchserve APIs on ports other than the default 8080 & 8081?
 Yes, Torchserve API ports are configurable using a properties file or environment variable.
-Refer [configuration.md](configuration.md) for more details.
+Refer to [configuration](configuration.md) for more details.
 
 
 ### How can I resolve model specific python dependency?
 You can provide a `requirements.txt` while creating a mar file using "--requirements-file/ -r" flag. Also, you can add dependency files using "--extra-files" flag.
-Refer [configuration.md](configuration.md) for more details.
+Refer to [configuration](configuration.md) for more details.
 
 ### Can I deploy Torchserve in Kubernetes?
 Yes, you can deploy Torchserve in Kubernetes using Helm charts.
-Refer [Kubernetes deployment ](../kubernetes/README.md) for more details.
+Refer [Kubernetes deployment ](https://github.com/pytorch/serve/blob/master/kubernetes/README.md#torchserve-kubernetes) for more details.
 
 ### Can I deploy Torchserve with AWS ELB and AWS ASG?
 Yes, you can deploy Torchserve on a multi-node ASG AWS EC2 cluster. There is a cloud formation template available [here](https://github.com/pytorch/serve/blob/master/examples/cloudformation/ec2-asg.yaml) for this type of deployment. Refer [ Multi-node EC2 deployment behind Elastic LoadBalancer (ELB)](https://github.com/pytorch/serve/tree/master/examples/cloudformation/README.md#multi-node-ec2-deployment-behind-elastic-loadbalancer-elb) more details.
 
 ### How can I backup and restore Torchserve state?
 TorchServe preserves server runtime configuration across sessions such that a TorchServe instance experiencing either a planned or unplanned service stop can restore its state upon restart. These saved runtime configuration files can be used for backup and restore.
-Refer [TorchServe model snapshot](snapshot.md#torchserve-model-snapshot) for more details.
+Refer to [TorchServe model snapshot](snapshot.md) for more details.
 
 ### How can I build a Torchserve image from source?
-Torchserve has a utility [script](../docker/build_image.sh) for creating docker images, the docker image can be hardware-based CPU or GPU compatible. A Torchserve docker image could be CUDA version specific as well.
+Torchserve has a utility [script](https://github.com/pytorch/serve/blob/master/docker/build_image.sh) for creating docker images, the docker image can be hardware-based CPU or GPU compatible. A Torchserve docker image could be CUDA version specific as well.
 
 All these docker images can be created using `build_image.sh` with appropriate options.
 
 Run `./build_image.sh --help` for all available options.
 
-Refer [Create Torchserve docker image from source](../docker/README.md#create-torchserve-docker-image) for more details.
+Refer to [Create Torchserve docker image from source](https://github.com/pytorch/serve/blob/master/docker/README.md#create-torchserve-docker-image) for more details.
 
 ### How to build a Torchserve image for a specific branch or commit id?
 To create a Docker image for a specific branch, use the following command:
@@ -91,11 +91,11 @@ The image created using Dockerfile.dev has Torchserve installed from source wher
 TorchServe looks for the config.property file according to the order listed in the [doc](https://github.com/pytorch/serve/blob/master/docs/configuration.md#configproperties-file). There is no override mechanism.
 
 ### What are model_store, load_models, models?
-- model_store: A mandatory argument during TorchServe start. It can be either defined in config.property or overridden by TorchServe command line option "[--model-store](https://github.com/pytorch/serve/blob/master/docs/configuration.md#command-line-parameters)".
+- model_store: A mandatory argument during TorchServe start. It can be either defined in config.property or overridden by TorchServe command line option "[--model-store](configuration.md)".
 
-- load_models: An optional argument during TorchServe start. It can be either defined in config.property or overridden by TorchServe command line option "[--models](https://github.com/pytorch/serve/blob/master/docs/configuration.md#command-line-parameters)".
+- load_models: An optional argument during TorchServe start. It can be either defined in config.property or overridden by TorchServe command line option "[--models](configuration.md)".
 
-- [models](https://github.com/pytorch/serve/blob/master/docs/configuration.md#command-line-parameters): Defines a list of models' configuration in config.property. A model's configuration can be overridden by [management API](https://github.com/pytorch/serve/blob/master/docs/management_api.md#register-a-model). It does not decide which models will be loaded during TorchServe start. There is no relationship b.w "models" and "load_models" (ie. TorchServe command line option [--models](https://github.com/pytorch/serve/blob/master/docs/configuration.md#command-line-parameters)).
+- [models](configuration.md): Defines a list of models' configuration in config.property. A model's configuration can be overridden by [management API](management_api.md). It does not decide which models will be loaded during TorchServe start. There is no relationship b.w "models" and "load_models" (ie. TorchServe command line option [--models](configuration.md)).
 
 ### 
 
@@ -108,43 +108,43 @@ You can use any tool like Postman, Insomnia or even use a python script to do so
 
 ### How can I add a custom API to an existing framework?
 You can add a custom API using **plugins SDK** available in Torchserve.
-Refer to [serving sdk](../serving-sdk) and [plugins](../plugins) for more details.
+Refer to [serving sdk](https://github.com/pytorch/serve/tree/master/serving-sdk) and [plugins](https://github.com/pytorch/serve/tree/master/plugins) for more details.
 
 ### How can pass multiple images in Inference request call to my model?
 You can provide multiple data in a single inference request to your custom handler as a key-value pair in the `data` object.
-Refer [this](https://github.com/pytorch/serve/issues/529#issuecomment-658012913) for more details.
+Refer to [this issue](https://github.com/pytorch/serve/issues/529#issuecomment-658012913) for more details.
 
 ## Handler
 Relevant documents
-- [Default handlers](default_handlers.md#torchserve-default-inference-handlers)
-- [Custom Handlers](custom_service.md#custom-handlers)
+- [Default handlers](default_handlers.md)
+- [Custom Handlers](custom_service.md)
 
 ### How do I return an image output for a model?
 You would have to write a custom handler and modify the postprocessing to return the image
-Refer [custom service documentation](custom_service.md#custom-handlers) for more details.
+Refer to [custom service documentation](custom_service.md) for more details.
 
 ### How to enhance the default handlers?
 Write a custom handler that extends the default handler and just override the methods to be tuned.
-Refer [custom service documentation](custom_service.md#custom-handlers) for more details.
+Refer to [custom service documentation](custom_service.md) for more details.
 
 ### Do I always have to write a custom handler or are there default ones that I can use?
 Yes, you can deploy your model with no-code/ zero code by using builtin default handlers.
-Refer [default handlers](default_handlers.md#torchserve-default-inference-handlers) for more details.
+Refer to [default handlers](default_handlers.md) for more details.
 
 ### Is it possible to deploy Hugging Face models?
 Yes, you can deploy Hugging Face models using a custom handler.
-Refer [HuggingFace_Transformers](https://github.com/pytorch/serve/blob/master/examples/Huggingface_Transformers/README.md) for example. 
+Refer to [HuggingFace_Transformers](https://github.com/pytorch/serve/blob/master/examples/Huggingface_Transformers/README.md#huggingface-transformers) for example. 
 
 ## Model-archiver
  Relevant documents
- - [Model-archiver ](../model-archiver/README.md#torch-model-archiver-for-torchserve)
- - [Docker Readme](../docker/README.md)
+ - [Model-archiver ](https://github.com/pytorch/serve/blob/master/model-archiver/README.md#torch-model-archiver-for-torchserve)
+ - [Docker Readme](https://github.com/pytorch/serve/blob/master/docker/README.md#docker-readme)
 
 ### What is a mar file?
 A mar file is a zip file consisting of all model artifacts with the ".mar" extension. The cmd-line utility `torch-model-archiver` is used to create a mar file.
 
 ### How can create mar file using Torchserve docker container?
-Yes, you create your mar file using a Torchserve container. Follow the steps given [here](../docker/README.md#create-torch-model-archiver-from-container).
+Yes, you create your mar file using a Torchserve container. Follow the steps given [here](https://github.com/pytorch/serve/blob/master/docker/README.md#create-torch-model-archiver-from-container).
 
 ### Can I add multiple serialized files in single mar file?
 Currently `torch-model-archiver` allows supplying only one serialized file with `--serialized-file` parameter while creating the mar. However, you can supply any number and any type of file with `--extra-files` flag. All the files supplied in the mar file are available in `model_dir` location which can be accessed through the context object supplied to the handler's entry point.
@@ -155,7 +155,7 @@ Sample code snippet:
 properties = context.system_properties
 model_dir = properties.get("model_dir")
 ```
-Refer [Torch model archiver cli](../model-archiver/README.md#torch-model-archiver-command-line-interface) for more details.
+Refer [Torch model archiver cli](https://github.com/pytorch/serve/blob/master/model-archiver/README.md#torch-model-archiver-command-line-interface) for more details.
 Relevant issues: [[#633](https://github.com/pytorch/serve/issues/633)]
 
 ### Can I download and register model using s3 presigned v4 url?

docs/index.md

@@ -4,20 +4,19 @@ TorchServe is a performant, flexible and easy to use tool for serving PyTorch mo
 
 
 ## ⚡ Why TorchServe
-* [Model Management API](https://github.com/pytorch/serve/blob/master/docs/management_api.md#management-api): multi model management with optimized worker to model allocation
-* [Inference API](https://github.com/pytorch/serve/blob/master/docs/inference_api.md#inference-api): REST and gRPC support for batched inference
+* [Model Management API](management_api.md): multi model management with optimized worker to model allocation
+* [Inference API](inference_api.md): REST and gRPC support for batched inference
 * [TorchServe Workflows](https://github.com/pytorch/serve/blob/master/examples/Workflows/README.md#workflow-examples): deploy complex DAGs with multiple interdependent models
 * Default way to serve PyTorch models in
   * [Kubeflow](https://v0-5.kubeflow.org/docs/components/pytorchserving/)
   * [MLflow](https://github.com/mlflow/mlflow-torchserve)
   * [Sagemaker](https://aws.amazon.com/blogs/machine-learning/serving-pytorch-models-in-production-with-the-amazon-sagemaker-native-torchserve-integration/)
   * [Kserve](https://kserve.github.io/website/0.8/modelserving/v1beta1/torchserve/): Supports both v1 and v2 API
   * [Vertex AI](https://cloud.google.com/blog/topics/developers-practitioners/pytorch-google-cloud-how-deploy-pytorch-models-vertex-ai)
-* Export your model for optimized inference. Torchscript out of the box, [ORT and ONNX](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md#performance-guide), [IPEX](https://github.com/pytorch/serve/tree/master/examples/intel_extension_for_pytorch), [TensorRT](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md#performance-guide), [FasterTransformer](https://github.com/pytorch/serve/tree/master/examples/FasterTransformer_HuggingFace_Bert)
-* [Performance Guide](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md#performance-guide): builtin support to optimize, benchmark and profile PyTorch and TorchServe performance
+* Export your model for optimized inference. Torchscript out of the box, [ORT and ONNX](https://github.com/pytorch/serve/blob/master/docs/performance_guide.md#performance-guide), [IPEX](https://github.com/pytorch/serve/tree/master/examples/intel_extension_for_pytorch), [TensorRT](performance_guide.md), [FasterTransformer](https://github.com/pytorch/serve/tree/master/examples/FasterTransformer_HuggingFace_Bert)
+* [Performance Guide](performance_guide.md): builtin support to optimize, benchmark and profile PyTorch and TorchServe performance
 * [Expressive handlers](https://github.com/pytorch/serve/blob/master/CONTRIBUTING.md#contributing-to-torchServe): An expressive handler architecture that makes it trivial to support inferencing for your usecase with [many supported out of the box](https://github.com/pytorch/serve/tree/master/ts/torch_handler)
-* [Metrics API](https://github.com/pytorch/serve/blob/master/docs/metrics.md#torchserve-metrics): out of box support for system level metrics with [Prometheus exports](https://github.com/pytorch/serve/tree/master/examples/custom_metrics), custom metrics and PyTorch profiler support
-
+* [Metrics API](metrics.md): out of box support for system level metrics with [Prometheus exports](https://github.com/pytorch/serve/tree/master/examples/custom_metrics), custom metrics and PyTorch profiler support
 ## 🤔 How does TorchServe work
 
 * [Serving Quick Start](https://github.com/pytorch/serve/blob/master/README.md#serve-a-model) - Basic server usage tutorial

ts_scripts/spellcheck_conf/wordlist.txt

@@ -1051,4 +1051,4 @@ largemodels
 torchpippy
 InferenceSession
 maxRetryTimeoutInSec
-neuronx
+neuronx