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How to Integrate NVIDIA GPU Cloud with Microsoft Azure

January 24, 2025

Discover how to seamlessly integrate NVIDIA GPU Cloud with Microsoft Azure for enhanced computing power and efficient AI workflows in this comprehensive guide.

How to Connect NVIDIA GPU Cloud to Microsoft Azure: a Simple Guide

 

Set Up and Configure Your Azure Account

 

  • Ensure you have an active Microsoft Azure account. You can sign up at the Azure website if you don't have one.

    •  

  • Access the Azure portal by navigating to https://portal.azure.com/.

    •  

  • In the Azure portal, ensure you have sufficient permissions to create resources and manage virtual networks.

 

Install Azure CLI

 

  • Download and install the Azure CLI from here.

    •  

  • After installation, open a terminal or command prompt and login using your Azure credentials:

 

az login

 

Register NVIDIA GPU Cloud (NGC)

 

  • Create an NVIDIA NGC account at NGC Signup.

    •  

  • After registration, verify your email and login to the NGC website.

    •  

  • Navigate to the 'API Key' section under your account settings. Generate and save this API key for accessing NGC resources.

 

Create an Azure Resource Group

 

  • In the Azure portal or using the CLI, create a new resource group to organize your NVIDIA GPU Cloud resources:

 

az group create --name myResourceGroup --location eastus

 

Deploy NVIDIA GPU-Optimized VM

 

  • Use the Azure Marketplace to search for NVIDIA GPU-optimized VMs or use the CLI to deploy a Deep Learning VM.

    •  

  • Run the following command to create a VM instance with an NVIDIA GPU:

 

az vm create \
  --resource-group myResourceGroup \
  --name myNvidiaVM \
  --image microsoft-dsvm:ubuntu-hvm:ubuntu-1804:latest \
  --size Standard_NC6

 

Install NVIDIA Drivers and Docker on Azure VM

 

  • SSH into the VM once it's running:

 

ssh azureuser@your-vm-ip-address

 

  • Update the package list and install the NVIDIA driver:

 

sudo apt-get update
cuda-drivers

 

  • Install Docker:

 

sudo apt-get install docker.io
sudo systemctl start docker
sudo systemctl enable docker

 

Configure Docker for NVIDIA GPU Support

 

  • Install the NVIDIA Docker container runtime:

 

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
sudo apt-get update

sudo apt-get install -y nvidia-docker2
sudo systemctl restart docker

 

  • Verify the installation using the NVIDIA Docker tool:

 

sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi

 

Access NVIDIA GPU Cloud CLI

 

  • Install the NGC CLI tool:

 

wget -O ngccli_cat_linux.zip https://ngc.nvidia.com/downloads/ngccli_cat_linux.zip && unzip ngccli_cat_linux.zip && rm ngccli_cat_linux.zip

 

  • Configure the NGC CLI with your API key obtained from the NGC website:

 

ngc config set

 

  • Enter your API key and set the default storage to Azure.

 

Pull and Run NVIDIA Docker Container

 

  • Use the NGC CLI to pull a Docker container with the desired framework:

 

ngc registry image pull nvcr.io/nvidia/tensorflow:21.03-tf1-py3

 

  • Run the pulled Docker container on your VM:

 

sudo docker run --gpus all -it nvcr.io/nvidia/tensorflow:21.03-tf1-py3

 

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How to Use NVIDIA GPU Cloud with Microsoft Azure: Usecases

 

Use NVIDIA GPU Cloud and Microsoft Azure for Deep Learning

 

  • Combine Forces: Leverage the power of NVIDIA GPU Cloud (NGC) with Microsoft Azure to build scalable deep learning models. This integration offers developers access to NGC's pre-trained models and AI software with Azure's vast computing resources and simplified machine learning tools.

    •  

  • Optimize Workflows: Utilize NVIDIA's GPU-optimized containers within Azure to streamline the process of training AI models. The containers are fully managed, offering a plug-and-play approach for data scientists aiming to leverage powerful GPU resources without the hassle of system maintenance.

    •  

  • Extreme Scalability: Azure's flexible architecture complements NGC's high performance. Deploying machine learning models on Azure using NVIDIA's technology ensures low latency and quick scaling abilities to handle varying workloads effectively.

    •  

  • Cost Efficiency: Both platforms allow usage-based billing, which can significantly reduce the cost of experimentation. Using Azure's spot instances with NVIDIA's scalable GPU solutions can optimize expense management.

 

Implementation Steps

 

  • Create an Azure Account: Sign up for Microsoft Azure and set up your subscription plan to begin leveraging Azure's cloud capabilities.

    •  

  • Access NVIDIA GPU Cloud: Register to access NVIDIA NGC and explore the curated set of software libraries, models, and tools specifically designed for AI, HPC, and data analytics.

    •  

  • Deploy GPU Instances in Azure: Utilize Azure's portal to create new virtual machines integrating NVIDIA GPUs. You can select from various options depending on the computational needs and budget.

    •  

  • Integrate NGC Containers: Pull relevant NGC containers into your Azure environment. These containers are equipped with the necessary software and libraries, easing setup procedures.

    •  

  • Conduct Experiments: Start training your deep learning models using TensorFlow, PyTorch, or any supported framework in the easy-to-deploy environment provided by NGC on Azure's infrastructure.

    •  

  • Monitor and Optimize Performance: Constantly assess your models' performance using Azure's monitoring tools and make necessary adjustments for better efficiency.

 

Code Example

 


docker pull nvcr.io/nvidia/tensorflow:<tag>

 

  • You can deploy this to your Azure-powered container on a GPU-enabled VM to start using NVIDIA's optimized TensorFlow framework.

 


az vm create --resource-group yourResourceGroup --name yourVM --image UbuntuLTS --size Standard_NC6 --admin-username azureuser

 

  • This command is an example illustrative of how you can create a VM in Azure optimized with NVIDIA GPUs, thereby boosting deep learning workflows when used in conjunction with NVIDIA NGC.

 

 

Transforming Healthcare AI with NVIDIA GPU Cloud and Microsoft Azure

 

  • Enhanced Diagnostics: Utilize NVIDIA GPU Cloud's (NGC) AI models in combination with Microsoft Azure's robust infrastructure to improve diagnostic accuracy. Leverage pre-trained imaging models from NGC on Azure’s AI-driven platform to facilitate faster and more accurate patient diagnostics.

    •  

  • Personalized Treatment: Apply deep learning models from NGC on Azure to tailor personalized treatment plans. By processing vast amounts of healthcare data on Azure GPU instances, AI can facilitate insights into specific patient needs, improving treatment outcomes.

    •  

  • Data Security and Compliance: With Azure's compliance certifications and NVIDIA’s secure containerized environments, manage patient data securely across AI workflows, adhering to critical health regulations such as HIPAA.

    •  

  • Healthcare AI Scalability: Azure's flexible scaling options allow healthcare providers to adjust resources as needed, testing various models from NGC. This helps tackle computational challenges during peak data processing hours effectively.

 

Implementation Steps

 

  • Initiate Azure Setup: Create a Microsoft Azure account to unlock cloud resources essential for handling healthcare applications efficiently.

    •  

  • Explore NVIDIA GPU Cloud Offerings: Register on NGC to access enhanced AI tools and medical imaging models optimized for Microsoft Azure environments.

    •  

  • Provision GPU Instances: Utilize Azure to set up GPU-powered virtual machines, specifically optimizing for workloads requiring substantial computation, like imaging diagnostics.

    •  

  • Deploy and Run NGC Containers: Use Azure’s container services to run NVIDIA's AI containers, thus integrating potent AI models into healthcare applications seamlessly.

    •  

  • Evaluate AI Models: Train and test advanced AI models for predictive analytics using Azure's machine learning operations (MLOps) tools to refine patient diagnosis systems.

    •  

  • Maintain Regulatory Standards: Employ Microsoft Azure's compliance tools to continually audit AI applications, ensuring they meet necessary healthcare regulation benchmarks.

 

Code Example

 

docker pull nvcr.io/nvidia/clara-train-sdk:<version>

 

  • This command pulls the NVIDIA Clara Train SDK, designed to enhance medical imaging tasks, which can be deployed directly onto a GPU-enabled Azure VM for advanced AI capabilities.

 

az vm create --resource-group healthcareGroup --name healthcareVM --image UbuntuLTS --size Standard_NC12 --admin-username azureuser

 

  • This Azure CLI command illustrates setting up a virtual machine that supports NVIDIA GPU, crucial for running complex healthcare AI models hosted on NGC, thus aiding in transformative patient care solutions.

 

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Troubleshooting NVIDIA GPU Cloud and Microsoft Azure Integration

Why is my NVIDIA GPU Cloud VM not showing up in my Azure portal?

 

Possible Causes for Missing VM

 

  • **Deployment Region Mismatch:** Ensure that your NVIDIA GPU Cloud VM was deployed in the same region you are checking in Azure portal.

    •  

  • **Incorrect Subscription Selection:** Verify that the correct subscription is selected in the Azure portal, as resources are subscription-bound.

    •  

  • **Resource Group Misplacement:** Check if the VM is in another resource group by searching all resource groups for the VM.

    •  

  • **Permissions Issues:** Ensure you have sufficient permissions to view the VM by confirming your role and access level in Azure IAM.

 

Troubleshooting Steps

 

  • Log into Azure using the CLI to verify VM existence. Run:

    ```shell
    az vm list --query "[?storageProfile.imageReference.offer=='NGC']" --output table
    ```

    •  

  • Check activity logs for any issues or errors related to the creation or deployment of the VM.

 

How can I optimize performance for GPU workloads on Azure with NVIDIA Cloud?

 

Choose the Right VM

 

  • Select VM instances with NVIDIA GPUs on Azure, like the NC, ND, or NV series, optimized for different tasks like compute or visualization.

 

Install Required Drivers

 

  • Ensure CUDA and NVIDIA drivers are properly installed. Use NVIDIA's provided scripts for seamless installation.

 


sudo apt-get update
sudo apt-get install nvidia-driver-<version>

 

Optimize CUDA Kernels

 

  • Profile and identify bottlenecks using NVIDIA Nsight or PyProf. Optimize memory access patterns and utilize shared memory.

 

Leverage Azure N-series Scripts

 

  • Utilize Azure's GPU optimization scripts to set up your environment correctly and automate configurations.

 

Monitor and Scale

 

  • Implement Azure Monitor and NVIDIA's DCGM to track performance, autoscale based on workload demands, and ensure sustained throughput.

 


from azure.mgmt.compute import ComputeManagementClient

# Example code snippet to monitor and scale

 

What are the steps to configure NVIDIA GPU drivers on Azure for NGC containers?

 

Prepare Your Azure VM

 

  • Choose a VM with an NVIDIA GPU, e.g., NC, ND, or NV series.

    •  

  • Ensure the VM size is compatible with NGC containers.

 

Install NVIDIA GPU Drivers

 

  • Connect to your VM via SSH.

    •  

  • Update the package list and install necessary packages: 
    sudo apt-get update 
sudo apt-get install -y build-essential

    •  

  • Download the NVIDIA driver from the official website and install it.

 

Set Up Docker

 

  • Install Docker: 
    sudo apt-get install -y docker.io

    •  

  • Install NVIDIA container toolkit: 
    distribution=$(. /etc/os-release;echo $ID$VERSION_ID) 
sudo curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - 
sudo curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list 
sudo apt-get update 
sudo apt-get install -y nvidia-docker2 
sudo systemctl restart docker

 

Run NGC Containers

 

  • Execute the desired NGC container ensuring CUDA support: 
    sudo docker run --gpus all nvcr.io/nvidia/tensorflow:latest

 

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