Ray on Azure ML

1. ray-on-aml version 1.1.8 or lower supports Spark from raydp with Delta Lake, Synapse JDBC and latest pyspark 3.2.1. Checkout spark examples
2. GPU & custom base image for interactive use: if you have GPU compute cluster, then either use ray_on_aml.getRay(gpu_support=True) which internally uses mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu18.04:20211221.v1 as base image. You can supply your own base image with ray_on_aml.getRay(base_image="YOUR_OWN_BASE_IMAGE")
3. VSCode is now supported. You can run interactive notebook with VSCode.

This package simplifies setup of core Ray and Ray's components such as Dask on Ray, Ray tune,Ray rrlib, Ray serve and Spark in Azure ML. It also comes with supports for high performance data access to Azure data sources such as Azure Storage, Delta Lake , Synapse SQL. It supports both interactive and job uses.

Architecture

Setup & Quick Start Guide

1. Prepare Azure ML environment

For interactive mode, setup a compute cluster and a compute instance in the same VNET.

Checklist

[ ] Azure Machine Learning Workspace

[ ] Virtual network/Subnet

[ ] Create Compute Instance in the Virtual Network

[ ] Create Compute Cluster in the same Virtual Network

2. Select kernel

Use a python 3.7+ conda environment from (Jupyter) Notebook in Azure Machine Learning Studio.

3. Install library

pip install --upgrade ray-on-aml

Installing this library will also install ray[tune]==1.12.0, ray[serve]==1.12.0, pyarrow>= 5.0.0, dask[complete]==2021.12.0, adlfs==2021.10.0, fsspec==2021.10.1, xgboost_ray==0.1.8, fastparquet==0.7.2

4. Run ray-on-aml

Run in interactive mode in a Compute Instance notebook

from ray_on_aml.core import Ray_On_AML
ws = Workspace.from_config()
ray_on_aml =Ray_On_AML(ws=ws, compute_cluster ="Name_of_Compute_Cluster", maxnode=3) 
ray = ray_on_aml.getRay() 
# may take 7 mintues or longer.Check the AML run under ray_on_aml experiment for cluster status.  

Note that by default,the library sets up your current compute instance as Ray head and all nodes in the remote compute cluster as workers. If you want to use one of the nodes in the remote AML compute cluster as head node and the remaining are worker nodes, simply pass ci_is_head=False to ray_on_aml.getRay(). To install additional library, use additional_pip_packages and additional_conda_packages parameters. The ray cluster will request 5 nodes from AML if maxnode is not specified.

ray_on_aml =Ray_On_AML(ws=ws, compute_cluster ="Name_of_Compute_Cluster", additional_pip_packages= \
['torch==1.10.0', 'torchvision', 'sklearn'])

For use in an Azure ML job, include ray_on_aml as a pip dependency and inside your script, do this to get ray Remember to use RunConfiguration(communicator='OpenMpi') in your AML job's ScriptRunConfig so that ray-on-aml can work correctly.

from ray_on_aml.core import Ray_On_AML
ray_on_aml =Ray_On_AML()
ray = ray_on_aml.getRay()

if ray: #in the headnode
    pass
    #logic to use Ray for distributed ML training, tunning or distributed data transformation with Dask

else:
    print("in worker node")

5. Ray Dashboard

The easiest way to view Ray dashboard is using the connection from VSCode for Azure ML. Open VSCode to your Compute Instance then open a terminal, type http://127.0.0.1:8265/ then ctrl+click to open the Ray Dashboard.

This trick tells VScode to forward port to your local machine without having to setup ssh port forwarding using VScode's extension on the CI.

6. Shutdown ray cluster

To shutdown cluster, run following.

ray_on_aml.shutdown()

7. Customize Ray version and the library's base configurations

Interactive cluster: There are two arguments in Ray_On_AML() class initilization to specify base configuration for the library with following default values.

Ray_On_AML(ws=ws, compute_cluster ="Name_of_Compute_Cluster",base_conda_dep =['adlfs==2021.10.0','pip==21.3.1'],\ 
base_pip_dep = ['ray[tune]==1.12.0','ray[rllib]==1.12.0','ray[serve]==1.12.0', 'xgboost_ray==0.1.6', 'dask==2021.12.0',\
'pyarrow >= 5.0.0','fsspec==2021.10.1','fastparquet==0.7.2','tabulate==0.8.9'])

You can change ray and other libraries versions. Do this with extreme care as it may result in conflicts impacting intended features of the package. If you change ray version here, you will need to manually re-install the ray library at the compute instance to match with the custom version of the cluster in case the compute instance is the head node. AML Job cluster: If you need to customize your ray version, you can do so by adding ray dependency after ray-on-aml. The reason is ray-on-aml comes with some recent ray version. It needs to be overidden. For example if you need ray 0.8.7, you can do like following in your job's env.yml file

      - ray-on-aml==0.1.8
      - ray[rllib,tune,serve]==0.8.7

Check out RLlib example with customized ray version to learn more

8. Quick start examples

Check out quick start examples to learn more

Contributing

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