Using Torchserve with Docker to Deploy my Classification Model: A Step-by-Step Guide
Deploying a machine learning model from scratch can often be a daunting task, rife with potential pitfalls that can diminish the performance of your service. Compatibility issues between your model serving framework and the model itself can lead to unexpected challenges, wasting valuable time and resources.
To address this, AWS and Meta have developed a specialized tool that promises to smooth out these wrinkles: Torchserve. This model serving framework is tailor-made for PyTorch, designed to bridge the gap between model development and production with less friction and more synergy.
We’re going to dive into what makes Torchserve a standout choice for PyTorch users. You’ll see how it’s making life easier for developers and why it might just be the solution you need to deploy your models quickly and without fuss.
TL;DR
Steps for Deploying Models with Torchserve & Docker:
- Install model archiver and build torchserve image.
- Select an appropriate default handler (such as image classification) or create a custom handler.
- Prepare the essential files for configuration
- Use torch-model-archiver to package the your model and handler…
