modal-serverless-gpu

Run serverless GPU workloads without managing infrastructure by using Modal's App and Function APIs to define Python-native ML jobs. Modal handles containerized environments, auto-scaling, and quick cold-starts to execute on-demand cloud hardware.

Deploy an ML model as an API endpoint using Modal by wrapping inference logic in a Python function decorated with Modal's App API. The function automatically provisions serverless GPUs and scales out to handle incoming API requests.

You can run multi-GPU training jobs in the cloud with Modal by specifying GPU options in your function definition. This provides on-demand cloud hardware access for conducting scalable ML experimentation and batch processing.

Execute batch GPU jobs on-demand by defining Python-native workflows through Modal's containerized environments. Modal applies auto-scaling to spin up serverless GPUs dynamically, processing batch ML pipelines efficiently without idle infrastructure costs.

Modal supports Python-native workflows for ML pipelines by using Modal's App, GPU spec options, and containerized environments. This enables seamless deployment and cloud hardware flexibility to run inference and training at scale.

Use serverless GPUs for ML experimentation to avoid the overhead of managing fixed infrastructure instances. Modal provides quick cold-start times and auto-scaling, enabling rapid on-demand experimentation without provisioning dedicated cloud hardware.