modal-serverless-gpu

Deploy machine learning models as serverless GPU APIs by defining Python-native infrastructure that provides on-demand GPU access and auto-scaling capabilities without managing servers.

Serverless GPU compute provides on-demand cloud GPU access for machine learning workloads, abstracting infrastructure management for model training, inference, and batch processing.

Yes, you can execute batch processing jobs like training and inference with automatic scaling by utilizing serverless GPU compute to run resource-intensive ML tasks efficiently.

No, this approach does not require provisioning your own GPU hardware, as it provides on-demand access to powerful cloud GPUs for complex inference tasks and model training.

Start by running the example Python script using the command `modal run hello_gpu.py`, which demonstrates how to execute GPU-intensive workloads without server management.

Yes, you need the modal dependency installed to utilize this Skill, as it relies on Python-native infrastructure definition to provide serverless GPU compute for machine learning workloads.