Add Client Quickstart (#102)

* Add Config CLI parser

and store config file.

* (wip, docs) Quickstart Page

* Revert "Add Config CLI parser"

In favour of another PR implementing config handling.

This reverts commit 8935a44.

* (fixup) Spellchecking of quickstart guide

Author: maxmynter

client/docs/quickstart.md

@@ -0,0 +1,70 @@
+# Jobq, workflow submission and scheduling
+
+Jobq is a tool that aims to make it easy for you to create, submit and schedule workflows.
+
+To get teams shipping fast, we keep the definition of the workflows inside a familiar environment: Python.
+You wrap all your logic into a function that you then decorate with the `@job` decorator.
+In this decorator you will define the environment and hardware requirements.
+
+You then use the `jobq` CLI to submit the workflow, stop it, or retrieve logs.
+
+The `jobq` backed is built on top of Kubernetes and is installed in the cluster, orchestrates the workflow management, and interfaces to the client CLI calls.
+
+Let us walk through an example.
+The example expects a set-up cluster and API server.
+
+## Workflow definition in your Python files
+
+To restructure your Python script into a workflow that can be executed by `jobq` you need to reorganize your logic such that everything is called from one top level function.
+Then you decorate this function with the `jobq.job` decorator.
+The decorator takes two arguments, `image` and `options`. 
+You use the `image` argument to submit `ImageOptions`, a wrapper specifying the image, that is the environment your code will execute in.
+The `ImageOptions` take a `spec`, that is a path to a `.yaml` or `Dockerfile` which outlines the environment creation, as well as the name and tag of the image.
+
+The other argument to the `job` decorator are `options`.
+Here you specify the desired resources using the `ResourceOptions` wrapper, can attach labels and specify the `SchedulingOptions`.
+
+A defined job looks similar to this
+```python
+# quickstart.py
+from pathlib import Path
+from jobq import ImageOptions, JobOptions, ResourceOptions, SchedulingOptions, job
+
+@job(
+    options=JobOptions(
+        labels={"type": "hello-world@quickstart"},
+        resources=ResourceOptions(memory='1Gi', cpu='1'),
+        scheduling=SchedulingOptions(
+            priority_class='background', queue_name="user-queue"
+        ),
+        image=ImageOptions(
+            spec=Path('dockerfile/path/relative/to/job'),
+            name="quickstart/hello-world",
+            tag="latest"
+        )
+    )
+)
+def quickstart():
+    print("Hello, World!")
+```
+
+## Interface with your workflows using the `jobq` CLI
+Now that we have the job defined let us execute it.
+You can execute the job via the CLI,`jobq submit quickstart.py`.
+This execution is locally, which may be useful for debugging and testing.
+However, you probably want to execute the job on the remote cluster. 
+In this case you can add the `--mode kueue` flag to submit the workflow to the cluster queue (orchestrated by Kubernetes Kueue) like so: 
+`jobq submit --mode kueue quickstart.py`.
+
+This returns the job id which we can use to fetch or stream the logs using `jobq logs <job id>`.
+
+## Setting up the `jobq` Backend and API server
+In this quickstart guide we use Minikube to run a Kubernetes cluster locally.
+In case you want to deploy the `jobq` backend on your own cluster, follow the instructions [here](/link/to/clustersetup).
+
+Follow the [ instructions ](https://minikube.sigs.k8s.io/docs/start/) to install Minikube in the Minikube docs. You will also need a container or virtual machine manager like [Docker](https://www.docker.com/get-started/).
+Then, open a terminal and start Minikube with `minikube start`.
+
+Next, you need to ensure that the registry add-on is enabled.
+Do so by running `minikube addons enable registry`.
+The registry is where the cluster saves the job images.