Compute Resources on Azure Machine Learning

Early concepts of machine learning came out back in 1950s but people were not able to explore the full power since most of the machine learning algorithms were largely computationally expensive. The computing power of the early systems were not enough to process large amounts of data using complex algorithms. Since cloud computing and GPUs opened the arena to perform complex computations, machine learning and deep learning got a boost and now used widely in many real-world applications.

As we discussed in the previous posts, being a leading cloud-based machine learning platform, Azure Machine Learning solves three main burdens in machine learning model development and deployment process.

  1. Setting up development environment with the burden of solving platform and software library dependencies.
  2. Setting up the computing environments (parallel processing libraries (CUDA) etc.)
  3. Setting and managing deployments

All of these 3 key areas in machine learning model development require some sort of a computing resource to create and manage. Azure machine learning service has centralized all the resources to a easy accessible portal allowing the developer to select the most suitable resource for their need.

Compute tab of the AML studio contains (as of the date am writing this) 4 main compute categories for specific purposes. Will go through each of those and see in where we can occupy them in our machine learning experiments.

Compute Instances –

No need of messing around with configuring CUDA and all python packages to set up the laptop for performing machine learning experiments or the data visualization. You can just go through few steps in a wizard to create a preconfigured computing instance on Azure. This is more similar for creating a virtual machine on Azure. If you need to use GPU based computing, you may have to select a N-series VM on Azure. (Make sure the region you selected is having the required VM families)

In order to do the experiments on the compute instance you can either use JupyterLab, Jupyter notebook, RStudio or through an SSH connection. You have to create a SSH public key on Azure to access the compute instance through SSH.

Tips –

  • GPU based compute instances are costly. Create such instances if you really need to do deep learning based experiments.
  • Think of a complex deep learning scenario where data preprocessing need large amount of CPU processing time while model training should be done using GPUs… You can use two compute instances where preprocessing happens in a CPU based instance while the GPU based expensive compute instance   is used for model training. (Connecting these two processes can be done using Azure machine learning pipelines)
  • Make sure to deallocate the resources when you are not using it. (Else you should have a fat wallet in your pocket)  

Training clusters –

Training clusters in Azure ML is the survivor when we are having complex computations to perform. You can perform tasks as Automated ML and hyperparameter tuning on these preconfigured clusters. The maximum number of nodes can be configured according to your need. Underlying technology behind the training clusters is docker containers. Simply you containerize your experiment and push into the cluster for computations/training.   

Inference clusters –

The end result of the experiments you perform sits on inference clusters. The web service endpoints you create can be deployed on this AKS based inference clusters. You can go for a low-cost inference cluster with few nodes for dev-test and a high performing cluster with many nodes according to the requirements in the production environment.  (normally we use ACI for dev-test and AKS for production web service end points)

Attached compute –

This is an interesting feature in Azure machine learning where you can push your machine learning workloads into external computing environments. Right now, AML is supporting

  • Azure Databricks
  • Data Lake Analytics
  • HDInsight
  • Virtual machine

Your VM should be running Ubuntu in order to connect as an attached compute.

Will discuss how to use these computing resources in your machine learning experiments in the future posts.

Happy coding! 😊  

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