modal-serverless-gpu

To deploy machine learning models as APIs for inference, use this Skill to wrap Python functions in serverless applications, exposing them as auto-scaling endpoints using the Modal client library.

Yes, you can run ML inference on serverless GPUs without infrastructure management by using this Skill to access cloud resources like T4, A100, and H100 GPUs for real-time applications.

You process batch jobs with automatic scaling by defining functions and class-based functions in Python, letting the platform handle scaling for training, inference, and data processing tasks.

Yes, you need the Modal client library for Python to set up serverless GPU workloads, as it is the required dependency for creating applications with defined functions and container images.

The best way to run real-time ML workloads without infrastructure overhead is deploying models as auto-scaling APIs, utilizing serverless GPUs to execute inference tasks directly in the cloud.

Yes, this approach supports both batch processing and real-time inference, allowing you to run training and data processing jobs while simultaneously deploying models as APIs for immediate requests.