Introduction
As the world becomes increasingly dependent on artificial intelligence (AI), the need for high-performance computing has never been more pressing. Arm’s high-performance processors, optimized for AI, have generated significant excitement. But how easily can ClearML work with GPUs paired with Arm-based CPU compute, compared to GPUs combined with x86 chips? In this article, we’ll explore the answer to this question.
Testing ClearML on an AWS Graviton with an Arm-based CPU
With all of the excitement around Arm’s high performance processors, which are optimized for AI, our team wanted to test how easily ClearML would work with GPUs paired with Arm-based CPU compute when compared to GPUs combined with x86 chips. We decided to run a project on AWS using a Graviton-based EC2 instance, and we chose the AWS Graviton2 processor, which is paired with NVIDIA’s T4G Tensor core GPUs for efficient inference.
We chose the AWS Graviton2 processor, which is designed around Arm’s 64-bit Neoverse N1 CPU, an Arm platform designed specifically for efficient high-performance cloud computing workloads. By successfully orchestrating ClearML workloads using AWS Graviton, we can confidently confirm that AI builders can frictionlessly take advantage of a more economical AWS computing option.
In designing our experiment, we decided to run some model training on an EC2 G5g machine on ClearML’s AI Platform using our own AWS autoscaler. To set up our instance, we found the right Amazon Machine Image (AMI) that matched the AWS instance type and established a budget (just as we would have to do for any other x86 AWS instance).
With the compute and orchestration set up, we ran our training job. It worked flawlessly without any hiccups. Even though this was what we expected, it still felt great to see confirmation that ClearML works on any hardware. Proof positive that AI builders can frictionlessly run jobs on less-expensive AWS Graviton instances using Arm-based CPUs!
Behind-the-Scenes: the Architecture that Enables the Magic
Although it works like magic, quite a lot went on behind the scenes to make the process seamless. Once a user sets up the orchestration and instance, ClearML’s silicon-agnostic design automates the rest by matching the container and bringing in the AI frameworks needed to support the hardware in run time. NVIDIA offers Arm+GPU software solutions similar to its x86+GPU offerings.
These improvements use Arm Kleidi technology available in the Arm Compute Library and Arm KleidiAI Library. On ClearML, AI builders have complete visibility over their entire AI workflow. In addition to monitoring the training job as it happens, ClearML brings the codebase and data into the container and monitors the performance of the instance itself in real time, displaying metrics, GPU utilization, CPU utilization, and network monitoring statistics.
The Benefits of Using ClearML + AWS Graviton with Arm-based CPUs
With ClearML, AI builders benefit from a silicon-agnostic platform that works on any CPU or GPU and can take advantage of more efficient computing options that are better optimized for their specific needs. AWS Graviton processors have been designed to deliver high performance at a lower price, and AI teams using Graviton EC2 instances can save up to 20% when compared to x86-based Amazon EC2 instances.
In addition, Graviton processors use up to 60% less energy than comparable EC2 instances on other architectures. As we’ve experienced ourselves, there are no barriers to using AWS’ more cost-effective computing. The experience of utilizing Graviton compute is exactly the same as running on an x86, and holds true for any other Arm processor as well.
Closing
To learn more about ClearML, please request a demo to speak with our sales team. For more information about deploying AI on Arm CPUs, learn more about Arm Kleidi. Stay tuned for our next Arm + ClearML blog about our experience running a model utilizing an Arm-based machine using llama.cpp.
Frequently Asked Questions
Question 1: What is Arm Kleidi technology?
Arm Kleidi technology is a set of tools and libraries that enable Arm-based CPUs to run machine learning frameworks more efficiently. It is available in the Arm Compute Library and Arm KleidiAI Library.
Question 2: What are the benefits of using ClearML with AWS Graviton?
ClearML provides a silicon-agnostic platform that works on any CPU or GPU, allowing AI builders to take advantage of more efficient computing options that are better optimized for their specific needs. Additionally, AWS Graviton processors deliver high performance at a lower price, with savings of up to 20% compared to x86-based Amazon EC2 instances.
Question 3: Can I run ClearML on any hardware?
Yes, ClearML works on any hardware, including Arm-based CPUs and x86 chips. The platform is designed to be silicon-agnostic, allowing AI builders to run their jobs on any hardware without worrying about compatibility issues.
Question 4: What is the architecture behind ClearML?
ClearML’s architecture is based on a silicon-agnostic design that automates the process of matching the container and bringing in the AI frameworks needed to support the hardware in run time. This allows AI builders to run their jobs on any hardware without worrying about compatibility issues.
Question 5: How does ClearML provide visibility into the AI workflow?
ClearML provides complete visibility into the AI workflow by bringing the codebase and data into the container and monitoring the performance of the instance itself in real time. This includes displaying metrics, GPU utilization, CPU utilization, and network monitoring statistics.
Conclusion
In conclusion, ClearML provides a silicon-agnostic platform that works on any CPU or GPU, allowing AI builders to take advantage of more efficient computing options that are better optimized for their specific needs. With the benefits of using ClearML with AWS Graviton, including high performance at a lower price, AI teams can save up to 20% compared to x86-based Amazon EC2 instances. We hope this article has provided valuable insights into the capabilities of ClearML and the benefits of using it with AWS Graviton.
