ray-on-aml 0.0.6

Turn AML cluster into Ray

Ray on Azure ML

This package simplifies setup of Ray and Ray's components such as DaskOnRay, SparkOnRay, Ray Machine Learning in Azure ML for your data science projects. This allows you to use AML compute as Ray/Dask cluster in both interactive and job modes.

Architecture

Prerequistes

Before you run sample, please check followings.

1. Configure Azure Environment

For Interactive use at your compute instance, create a compute cluster in the same vnet where your compute instance is, then run this to get handle to the ray cluster

Check list

[ ] 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 azureml_py38 conda environment from (Jupyter) Notebook in Azure Machine Learning Studio.

Note: Due to Conda env issue,VSCode is not supported yet when using the compute instance as head node when using with ci_is_head = True in getRay() method

3. Install library

pip install --upgrade ray-on-aml

Installing this library will also install ray[default]==1.9.1, pyarrow>= 5.0.0, dask[complete]==2021.12.0, adlfs==2021.10.0 and fsspec==2021.10.1

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")
ray = ray_on_aml.getRay() # may take around 7 or more mintues

Note that by default, one of the nodes in the remote AML compute cluster is used as head node and the remaining are worker nodes. But if you want to use your current compute instance as head node and all nodes in the remote compute cluster as workers Then simply specify ci_is_head=True). To install additional library, use additional_pip_packages and additional_conda_packages parameters.

ray_on_aml =Ray_On_AML(ws=ws, compute_cluster ="d15-v2", additional_pip_packages=['torch==1.10.0', 'torchvision', 'sklearn'], maxnode=4)
ray = ray_on_aml.getRay(ci_is_head=True)

Advanced usage:There are two arguments to Ray_On_AML() object initilization with to specify base configuration for the library with following default values. Although it's possible, you should not change the default values of base_conda_dep and base_pip_dep as it may break the package. Only do so when you need to customize the cluster default configuration such as ray version.

Ray_On_AML(ws=ws, compute_cluster ="Name_of_Compute_Cluster",base_conda_dep =['adlfs==2021.10.0','pip'],base_pip_dep = ['ray[tune]==1.9.1', 'xgboost_ray==0.1.5', 'dask==2021.12.0','pyarrow >= 5.0.0','fsspec==2021.10.1'])

For use in an Azure ML job, include ray_on_aml as a pip dependency and inside your script, do this to get ray

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. Shutdown ray cluster

To shutdown cluster you must run following.

ray_on_aml.shutdown()

Check out quick start examples to learn more