Using Dask-ML on Azure ML

This repository contains a simple demo of how to run dask-ml functions on an Azure ML compute cluster. The demo takes advantage of dask-mpi to simplify cluster setup.

Instructions

Install Anaconda or Miniconda

Create and activate a Python 3 environment:

  conda create azureml
  conda activate azureml

Install Azure ML SDK:
```
  pip install azureml-sdk
```
Create a new Azure ML workspace

Clone this repository and create a config.json file in the repository directory containing your Azure ML subscription, tenant ID, resource group, workspace name, and your preferred names for the compute cluster and experiment. The file should look like the following:

  {
      "tenant_id": "WWWWWWWW-WWWW-WWWW-WWWW-WWWWWWWWWWWW",
      "subscription_id":"XXXXXXXX-XXXX-XXXX-XXXX-XXXXXXXXXXXX", 
      "resource_group": "YYYYYYYY",
      "workspace_name": "ZZZZZZZZ",
      "compute_name": "AAAAAAAA",
      "experiment_name": "BBBBBBBB"
  }

Run the demo as follows:
```
  python run.py
```
Once the demo has finished, you can view the results in the Azure portal.

lebedov/dask-ml-on-azure-ml

Using Dask-ML on Azure ML

Instructions