This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging.
This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.
It’s easy to use ClearML handlers with the MONAI Toolkit to accelerate AI development for medical workflows.
For technical information on the integration, check out our documentation, our GitHub page, or MONAI’s documentation on ClearML handlers.
Get started with ClearML by using our free tier servers or by hosting your own. Read our documentation here. You’ll find more in-depth tutorials about ClearML on our YouTube channel and we also have a very active Slack channel for anyone that needs help.
If you need to scale your ML pipelines and data abstraction or need unmatched performance and control, please request a demo. To learn more about ClearML, please visit: https://clear.ml/.
MONAI Enterprise is available as part of NVIDIA AI Enterprise, a secure, end-to-end, cloud native software platform that accelerates the data science pipeline and streamlines development and deployment of production-grade AI applications. The MONAI Toolkit includes a base container and library of pre-trained models that make it easier for AI teams to get started. Visit the NVIDIA MONAI website to learn more: https://www.nvidia.com/en-us/clara/monai.
