Add dev quick start guide

docs/quickstart.md

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+---
+layout: default
+title: Dev Quick Start Guide
+nav_order: 7
+---
+
+# Dev Quick Start Guide
+{: .no_toc }
+
+This document describes how to quickly get a runtime server built with `sample_lib` library, train a model with gRPC and with that trained model, send an inference call to the server with either HTTP or gRPC call.
+
+## Pre-reqs
+To set up development environment and tooling, the [Development Guide]({{ site.baseurl }}{% link docs/developing.md %}) page is a good place to start.
+
+## Build and start a runtime server with sample_lib
+
+Run the `start_runtime_with_sample_lib` python script:
+
+```shell
+python3 -m examples.sample_lib.start_runtime_with_sample_lib
+```
+
+This will setup a config with both `grpc` and `http` servers enabled for inference service, as well as training service. The library used with some sample modules is `sample_lib`. The script then starts the caikit runtime server. While the server is running, you can see the generated protobufs in a directory called `protos`. 
+
+We generate 3 services total:
+- A train service. The proto for this service is `protos/samplelibtrainingservice.proto`
+- An inference service. The proto for this service is `protos/samplelibservice.proto`
+- A training management service. The proto for this service is `protos/trainingmanagement.proto`
+
+You can now leave the server running and open a new terminal to proceed with next steps to train a model, check its training status and send an inference request to your model.
+
+(To kill the server, press Ctrl + C. This will remove the `protos` directory to clean up.)
+
+## To train a model
+
+As of caikit v`0.18.0`, we do not yet support training model via REST.
+In order to train a model via gRPC, we will use `grpcurl` and point the import-path to `protos` dir, then call one of the Train rpc's available in the `SampleLibTrainingService` (see `protos/samplelibtrainingservice.proto` file generated above for all Train rpcs):
+
+```shell
+grpcurl -plaintext -import-path protos/ -proto samplelibtrainingservice.proto -d '{"model_name": "my_model", "training_data": {"file": {"filename": "protos/sample.json"}}}' localhost:8085 caikit.runtime.SampleLib.SampleLibTrainingService/SampleTaskSampleModuleTrain
+```
+
+You should receive a response similar to the below:
+
+```shell
+{
+  "trainingId": "wTHxlsu:5bdb5949-4efa-4512-bbac-709cbf37c00e",
+  "modelName": "my_model"
+}
+```
+
+Copy the `trainingId` to use in next step.
+
+## To check on training status of a trainingId
+
+With a `trainingId`, you can get a training status via gRPC. Replace the command below with your `trainingId`.
+
+```shell
+grpcurl -plaintext -import-path protos/ -proto trainingmanagement.proto -d '{"training_id": "<training_id"}' localhost:8085 caikit.runtime.training.TrainingManagement/GetTrainingStatus
+```
+
+You should get a response like this:
+
+```shell
+{
+  "trainingId": "wTHxlsu:5bdb5949-4efa-4512-bbac-709cbf37c00e",
+  "state": "COMPLETED",
+  "submissionTimestamp": "2023-08-30T22:19:13.739694Z",
+  "completionTimestamp": "2023-08-30T22:19:13.744542Z"
+}
+```
+Once your training is completed, you can proceed to call inference on the model.
+
+## To call inference on a model with modelId
+
+You are now ready to call inference via either gRPC or REST.
+
+### To use the REST Server for inference
+
+- In a browser of choice, visit `http://localhost:8080/docs/`. All the available inference rpcs are listed. Expand on the correct task for the model you trained. In this example, we are using `api/v1/{model_id}/task/sample`.
+- Click "Try It Out"
+- Fill in model_id "my_model" as used in the train a model step. Change the request body to your liking. Then click "Execute". Ex:
+
+```shell
+curl -X 'POST' \
+  'http://localhost:8080/api/v1/my_model/task/sample' \
+  -H 'accept: application/json' \
+  -H 'Content-Type: application/json' \
+  -d '{
+  "inputs": {
+    "name": "IBM"
+  }
+}'
+```
+
+You should receive a 200 response back with a response body:
+```shell
+{
+  "greeting": "Hello IBM"
+}
+```
+
+### To use the gRPC Server for inference
+
+You can also use the gRPC Server to call inference on this model by running:
+```shell
+grpcurl -plaintext -import-path protos/ -proto samplelibservice.proto -d '{"sample_input": {"name": "IBM"}}' -H 'mm-model-id: my_model' localhost:8085 caikit.runtime.SampleLib.SampleLibService/SampleTaskPredict
+```
+
+You should receive a sucessful response back with a response body:
+```shell
+{
+  "greeting": "Hello IBM"
+}
+```