Master Microsoft Azure: Batch AI, Durable Functions & More
Table of Contents:
- Introduction
- Microsoft Azure: An Overview
- Batch AI: Training and Testing Deep Learning Models
- Setting Up Batch AI
- Configuring Default Values
- Downloading Data
- Creating Azure File Share
- Copying Files to File Share
- Creating Batch AI Cluster
- Running Batch AI Job
- Downloading Job Configuration File
- Submitting the Job
- Monitoring Progress
- Checking Output and Error Information
- Visual Studio 2017: New Features
- Durable Functions: Long-Running Stateful Workflows
- Orchestrator Functions
- Function Chaining and Fan-In/Fan-Out
- Functions with Durable Timers
- Persistence and Execution Management
- Monitoring with Application Insights
- Language Support and Future Updates
- Azure Database for PostgreSQL and MySQL: Public Preview
- Azure File Shares Snapshots: Read-Only Versions
- Accessing Previous Versions of Files
- Restoring Files from Snapshots
- Managing Snapshots through the Azure Portal
- Azure Management Libraries for .NET: Updates and New Features
- Virtual Network Peering
- Virtual Private Network Connections
- Azure Container Instances
- Availability Zone Support
Introduction
In this article, we will explore the latest news and updates about Microsoft Azure, a cloud computing platform and service provided by Microsoft. We will focus specifically on Microsoft Azure's Batch AI service, which provides a platform for training and testing deep learning and other AI models using managed clusters of GPUs. We will discuss the setup process for Batch AI, how to run a job, and explore some other exciting features and updates in the Microsoft Azure ecosystem.
Microsoft Azure: An Overview
Microsoft Azure is a cloud computing platform that offers a wide range of services including networking, storage, virtual machines, databases, AI, and more. It provides organizations with the flexibility to build, deploy, and manage applications and services from anywhere, using tools and frameworks of their choice. With a global network of secure data centers, Azure ensures high availability, scalability, and reliability for its customers.
Batch AI: Training and Testing Deep Learning Models
Batch AI is a new service introduced by Microsoft Azure that simplifies the process of training and testing deep learning and other AI or machine learning models. It allows users to provision clusters of GPUs on-demand, install software through containers or scripts, automate scaling, reduce costs with low priority virtual machines, and use shared storage volumes for training and output data. It works seamlessly with various deep learning frameworks and provides recipes to help users get up and running quickly.
Setting Up Batch AI
Before we dive into running jobs on Batch AI, let's go through the setup process step by step.
Step 1: Configuring Default Values
To minimize repetition in the commands, it's recommended to set some default values. These values include the default location (east us in this example) and the default resource group to use. Additionally, environment variables can be set to hold details of the storage account and storage account keys that will be used.
Step 2: Downloading Data
In order to use Batch AI for training and testing, we need to have data. This data can be downloaded from a GitHub repository or any other source that provides the required dataset. Once the data is downloaded, it can be stored in a local storage location for further processing.
Step 3: Creating Azure File Share
Batch AI requires a file share to store the training and output data. In this step, we will Create an Azure File Share and define its name and share name. This file share will be used during the job execution.
Step 4: Copying Files to File Share
After creating the file share, we need to copy the files that will be used for the Batch AI job to the Azure File Share. This can be done using the AZ storage file upload command, which will upload the required files to the specified file share.
Step 5: Creating Batch AI Cluster
Now, it's time to create the Batch AI cluster. This cluster will provide the computational resources necessary for training and testing the AI models. The cluster can be created using the Azure CLI or through the Azure portal. The command to create the cluster starts with AZ batch AI cluster create, followed by the name of the cluster and the image to be used (such as the data science virtual machine in this example). The cluster can be scaled up and down as per requirements.
Running Batch AI Job
With the cluster created, we can now move on to running a Batch AI job. This job will utilize the resources provided by the cluster to perform the training or testing tasks. Let's go through the steps involved in running a Batch AI job.
Step 1: Downloading Job Configuration File
To specify the details of the job, we need a job configuration file. This file can be downloaded from a GitHub repository that provides sample job configurations. It contains all the necessary information to define the job, such as the job name, the cluster to target, and other job-specific details.
Step 2: Submitting the Job
Once we have the job configuration file, we can submit the job using the AZ batch AI job create command. This command takes the job name, the cluster to target, and the path to the job configuration file as parameters. After submitting the job, we can monitor its progress and view its status through the Azure portal.
Step 3: Monitoring Progress
Through the Azure portal, we can monitor the progress of the Batch AI job. We can see the details of the cluster nodes and the file share being used. We can also keep track of the job's Current status, view output and error information, and ensure that everything is running smoothly.
Step 4: Checking Output and Error Information
Once the job is complete, we can check the standard output file to make sure that everything ran as expected. This file contains the output generated by the job and can be used for further analysis or evaluation. Additionally, we can also view the output of the job in the Azure File Share, where the training or testing results are stored.
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Highlights:
- Microsoft Azure offers a wide range of services for cloud computing.
- Batch AI simplifies training and testing of deep learning models using managed GPU clusters.
- Setting up Batch AI involves configuring default values, downloading data, creating an Azure File Share, and copying files to the file share.
- Running a Batch AI job requires a job configuration file, which can be downloaded from a GitHub repository.
- Batch AI jobs can be submitted and monitored through the Azure portal.
- Durable Functions in Azure enable the creation of long-running stateful workflows.
- Azure offers support for PostgreSQL and MySQL databases in public preview.
- Azure File Shares now support snapshots, allowing for the restoration of previous file versions.
- Azure Management Libraries for .NET have been updated to include virtual network peering, site-to-site connections, and availability zone support.
FAQ:
Q: Can Batch AI be used with any deep learning framework?
A: Yes, Batch AI works with any deep learning framework and provides recipes to help users get started with popular frameworks.
Q: Can Batch AI clusters be scaled up and down?
A: Yes, Batch AI clusters can be scaled up and down based on the workload and resource requirements.
Q: Is the job progress visible in the Azure portal?
A: Yes, the Azure portal provides a monitoring interface to track the progress of Batch AI jobs, view output and error information, and ensure smooth execution.
Q: Can durable functions be used with languages other than C#?
A: Currently, durable functions are available in C# only, but support for other languages is in the works.
Q: How can I restore files from Azure File Share snapshots?
A: Files can be restored from Azure File Share snapshots by accessing the previous versions feature in Windows or through the Azure portal.
Q: What are the new features in Azure Management Libraries for .NET?
A: The updated Azure Management Libraries for .NET include support for virtual network peering, site-to-site connections, Azure container instances, and availability zone support.
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