TorchServe

TorchServe is a performant, flexible and easy to use tool for serving PyTorch eager mode and torchscripted models.

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 model-archiver.

• 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

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

Examples

• 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

• Dual Translation Workflow -

• 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

  • TorchServe Internals

  • TorchServe Integrations

  • TorchServe UseCases

• Workflow Examples - Examples of how to compose models in a workflow with TorchServe

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

• NVIDIA MPS - Use NVIDIA MPS to optimize multi-worker deployment on a single GPU

  kick