Provision an AzureML workspace to run the LightGBM benchmark

Objectives - By following this tutorial, you will be able to setup the Azure resources you need to run the pipelines in this repo.

Requirements - To enjoy this tutorial, you need to have a working Azure account and subscription (see how to create one, or how to get Visual Studio Enterprise benefits).

Option A. Create an AzureML workspace (manual route)

1. If you don't have one already, create an AzureML workspace.

2. In that workspace, you will need to create compute clusters. Here's what we recommend to get you started with LightGBM training using the pipelines of this repo. The names below are indicative, but we'll keep refering to those in our docs.

   Cluster Name	SKU	Node count	Description
   cpu-cluster	Standard_DS3_v2	4	A cluster for simple jobs, running on cheap VMs.
   linux-cpu-d32sv3	Standard_D32s_v3	10	A cluster for LightGBM itself, with a more powerful yet affordable VM. Feel free to provision more or less.
   linux-gpu-nv6	Standard_NV6 (gpu)	1	Optional: for trying out gpu lightgbm training (work in progress)

   IMPORTANT: Whenever you create those, set the minimum number of nodes to 0 so that unused clusters will automatically size down and reduce costs.

Option B. Create an AzureML workspace for LightGBM using an ARM template

Work in progress, feel free to contribute to the discussion on this topic in the github repo.