TorchServe is a performant, flexible and easy to use tool for serving PyTorch eager mode and torschripted models.
1.1. Basic Features¶
Serving Quick Start - Basic server usage tutorial
Model Archive Quick Start - Tutorial that shows you how to package a model archive file.
Installation - Installation procedures
Serving Models - Explains how to use TorchServe
REST API - Specification on the API endpoint for TorchServe
gRPC API - TorchServe supports gRPC APIs for both inference and management calls
Packaging Model Archive - Explains how to package model archive file, use
Inference API - How to check for the health of a deployed model and get inferences
Management API - How to manage and scale models
Logging - How to configure logging
Metrics - How to configure metrics
Prometheus and Grafana metrics - How to configure metrics API with Prometheus formatted metrics in a Grafana dashboard
Captum Explanations - Built in support for Captum explanations for both text and images
Batch inference with TorchServe - How to create and serve a model with batch inference in TorchServe
Workflows - How to create workflows to compose Pytorch models and Python functions in sequential and parallel pipelines
1.2. Default Handlers¶
Image Classifier - This handler takes an image and returns the name of object in that image
Text Classifier - This handler takes a text (string) as input and returns the classification text based on the model vocabulary
Object Detector - This handler takes an image and returns list of detected classes and bounding boxes respectively
Image Segmenter- This handler takes an image and returns output shape as [CL H W], CL - number of classes, H - height and W - width
HuggingFace Language Model - This handler takes an input sentence and can return sequence classifications, token classifications or Q&A answers
Multi Modal Framework - Build and deploy a classifier that combines text, audio and video input data
Model Zoo - List of pre-trained model archives ready to be served for inference with TorchServe.
Examples - Many examples of how to package and deploy models with TorchServe
Workflow Examples - Examples of how to compose models in a workflow with TorchServe
1.4. Advanced Features¶
Advanced configuration - Describes advanced TorchServe configurations.
A/B test models - A/B test your models for regressions before shipping them to production
Custom Service - Describes how to develop custom inference services.
Encrypted model serving - S3 server side model encryption via KMS
Snapshot serialization - Serialize model artifacts to AWS Dynamo DB
Benchmarking and Profiling - Use JMeter or Apache Bench to benchmark your models and TorchServe itself
TorchServe on Kubernetes - Demonstrates a Torchserve deployment in Kubernetes using Helm Chart supported in both Azure Kubernetes Service and Google Kubernetes service
mlflow-torchserve - Deploy mlflow pipeline models into TorchServe
Kubeflow pipelines - Kubeflow pipelines and Google Vertex AI Managed pipelines