TorchServe
PyTorch native model serving framework for deploying and managing ML models in production
TorchServe
PyTorch native model serving framework for deploying and managing ML models in production
TorchServe is a flexible, easy-to-use tool for serving PyTorch eager mode and TorchScripted models developed by AWS and Facebook. It enables data scientists and ML engineers to deploy trained PyTorch models as REST APIs without writing server code. TorchServe handles multi-model serving, batching, logging, metrics, and model versioning out of the box. Teams building PyTorch-based applications use TorchServe to move from model notebook to production API quickly. The framework integrates with AWS SageMaker for managed deployment but can also run on any infrastructure. Custom handlers allow serving any PyTorch model type including NLP, computer vision, and recommendation systems.
Key Features
- ✓PyTorch native
- ✓Multi-model serving
- ✓Batching support
- ✓REST API
- ✓Model versioning
Quick Info
- Category
- AI Infrastructure
- Pricing
- Free
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