Accelerate your AI Workloads with Ampere Optimized Frameworks

Table of Contents

1. Introduction
2. Ampere Optimized AI Frameworks
3. Supported AI Frameworks
4. Accessing Ampere Optimized Frameworks on Azure Virtual Machines
5. Steps to Create an Ampere Ultra-Based Azure Virtual Machine
6. How to Run AI Workloads on Ampere Ultra-Based Azure Virtual Machines
7. Benefits of Ampere Optimized Frameworks on Azure Virtual Machines
8. Future Availability of Ampere AI Software on Azure Marketplace
9. Contacting the Ampere AI Support Team
10. Conclusion

Ampere Optimized Frameworks for Running AI Workloads on Azure Virtual Machines

Are You looking for a way to accelerate the performance of your AI workloads on Azure Virtual Machines? Look no further than Ampere Computing's optimized AI Frameworks. Designed by our team of software engineers, these frameworks support all major AI frameworks, including PyTorch, TensorFlow, and ONNX Runtime, and can deliver two to five times additional performance for AI workloads that are running on Ampere Cloud-native processors.

Ampere Optimized AI Frameworks

Our software stack is designed to accelerate the performance of AI workloads on Ampere processors. The stack is constantly updated and optimized by our team to ensure maximum performance for users. It is a plug-and-play library that requires no API changes or additional coding on your part, making it easy to use right out of the box.

Supported AI Frameworks

Our optimized frameworks support all major AI frameworks, including PyTorch, TensorFlow, and ONNX Runtime. If your application is developed using one of these supported frameworks, our software stack will integrate with it seamlessly.

Accessing Ampere Optimized Frameworks on Azure Virtual Machines

Accessing Ampere Optimized Frameworks on Azure Virtual Machines is simple, requiring only a few easy steps. First, you will need to log in to Microsoft Azure and click "Create a Resource" before typing in "Virtual Machine" into the search bar and selecting the virtual machine offering. From there, select the subscription and resource group you want to create the virtual machine in before naming your new virtual machine and clicking "See all images."

Steps to Create an Ampere Ultra-Based Azure Virtual Machine

1. Click on "Community Images Preview"
2. Type "Ampere" into the search bar
3. Select the virtual machine labeled "Ampere Computing AI"
4. Under the publisher field, select the separate image for each supported framework
5. Choose the virtual machine size that fits your needs, and change any other Relevant fields according to your needs
6. Click "Review + Create"
7. After the successful validation of the VM template, click "Create"

How to Run AI Workloads on Ampere Ultra-Based Azure Virtual Machines

Once you have successfully created your Ampere Ultra-Based Azure Virtual Machine and installed the Ampere Optimized Frameworks, you can begin running your AI workloads with maximum performance acceleration. Simply SSH into your newly created virtual machine and get started.

Benefits of Ampere Optimized Frameworks on Azure Virtual Machines

The benefits of using Ampere Optimized Frameworks on Azure Virtual Machines are numerous. These frameworks deliver two to five times additional performance for AI workloads, thus enabling developers to satisfy all the relevant AI workload performance requirements. Additionally, they do not require any API changes or additional coding on your part, making them incredibly easy to use.

Future Availability of Ampere AI Software on Azure Marketplace

We invite you to follow updates from Ampere and Azure about the future availability of Ampere AI software on the Azure Marketplace, as well as to hear about new releases of Ampere Optimized Frameworks.

Contacting the Ampere AI Support Team

If you need any additional technical support or information about Ampere Cloud-native processors, please feel free to reach out to the Ampere AI team by email at AI-support@amperecomputing.com.

Conclusion

In conclusion, Ampere Optimized Frameworks are an excellent way to accelerate the performance of your AI workloads on Azure Virtual Machines. With their seamless integration and easy-to-use plug-and-play library, they offer developers a quick and simple way to maximize the performance of their AI workloads without the need for additional coding or API changes.