TorchServe is a flexible and easy-to-use tool for serving and scaling PyTorch models in production.

Requires python >= 3.8

curl http://127.0.0.1:8080/predictions/bert -T input.txt

# Install dependencies
# cuda is optional
python ./ts_scripts/install_dependencies.py --cuda=cu121

# Latest release
pip install torchserve torch-model-archiver torch-workflow-archiver

# Nightly build
pip install torchserve-nightly torch-model-archiver-nightly torch-workflow-archiver-nightly

# Install dependencies
# cuda is optional
python ./ts_scripts/install_dependencies.py --cuda=cu121

# Latest release
conda install -c pytorch torchserve torch-model-archiver torch-workflow-archiver

# Nightly build
conda install -c pytorch-nightly torchserve torch-model-archiver torch-workflow-archiver

Getting started guide

# Latest release
docker pull pytorch/torchserve

# Nightly build
docker pull pytorch/torchserve-nightly

Refer to torchserve docker for details.

• Write once, run anywhere, on-prem, on-cloud, supports inference on CPUs, GPUs, AWS Inf1/Inf2/Trn1, Google Cloud TPUs, Nvidia MPS
• Model Management API: multi model management with optimized worker to model allocation
• Inference API: REST and gRPC support for batched inference
• TorchServe Workflows: deploy complex DAGs with multiple interdependent models
• Default way to serve PyTorch models in
  • Sagemaker
  • Vertex AI
  • Kubernetes with support for autoscaling, session-affinity, monitoring using Grafana works on-prem, AWS EKS, Google GKE, Azure AKS
  • Kserve: Supports both v1 and v2 API, autoscaling and canary deployments for A/B testing
  • Kubeflow
  • MLflow
• Export your model for optimized inference. Torchscript out of the box, PyTorch Compiler preview, ORT and ONNX, IPEX, TensorRT, FasterTransformer, FlashAttention (Better Transformers)
• Performance Guide: builtin support to optimize, benchmark, and profile PyTorch and TorchServe performance
• Expressive handlers: An expressive handler architecture that makes it trivial to support inferencing for your use case with many supported out of the box
• Metrics API: out-of-the-box support for system-level metrics with Prometheus exports, custom metrics,
• Large Model Inference Guide: With support for GenAI, LLMs including
  • SOTA GenAI performance using torch.compile
  • Fast Kernels with FlashAttention v2, continuous batching and streaming response
  • PyTorch Tensor Parallel preview, Pipeline Parallel
  • Microsoft DeepSpeed, DeepSpeed-Mii
  • Hugging Face Accelerate, Diffusers
  • Running large models on AWS Sagemaker and Inferentia2
  • Running Llama 2 Chatbot locally on Mac
• Monitoring using Grafana and Datadog

• Model Server for PyTorch Documentation: Full documentation
• TorchServe internals: How TorchServe was built
• Contributing guide: How to contribute to TorchServe

• Serving Llama 2 with TorchServe
• Chatbot with Llama 2 on Mac 🦙💬
• 🤗 HuggingFace Transformers with a Better Transformer Integration/ Flash Attention & Xformer Memory Efficient
• Stable Diffusion
• Model parallel inference
• MultiModal models with MMF combining text, audio and video
• Dual Neural Machine Translation for a complex workflow DAG
• TorchServe Integrations
• TorchServe Internals
• TorchServe UseCases

For more examples

SECURITY.md

https://pytorch.org/serve

We welcome all contributions!

To learn more about how to contribute, see the contributor guide here.

• High performance Llama 2 deployments with AWS Inferentia2 using TorchServe
• Naver Case Study: Transition From High-Cost GPUs to Intel CPUs and oneAPI powered Software with performance
• Run multiple generative AI models on GPU using Amazon SageMaker multi-model endpoints with TorchServe and save up to 75% in inference costs
• Deploying your Generative AI model in only four steps with Vertex AI and PyTorch
• PyTorch Model Serving on Google Cloud TPU v5
• Monitoring using Datadog
• Torchserve Performance Tuning, Animated Drawings Case-Study
• Walmart Search: Serving Models at a Scale on TorchServe
• 🎥 Scaling inference on CPU with TorchServe
• 🎥 TorchServe C++ backend
• Grokking Intel CPU PyTorch performance from first principles: a TorchServe case study
• Grokking Intel CPU PyTorch performance from first principles( Part 2): a TorchServe case study
• Case Study: Amazon Ads Uses PyTorch and AWS Inferentia to Scale Models for Ads Processing
• Optimize your inference jobs using dynamic batch inference with TorchServe on Amazon SageMaker
• Using AI to bring children's drawings to life
• 🎥 Model Serving in PyTorch
• Evolution of Cresta's machine learning architecture: Migration to AWS and PyTorch
• 🎥 Explain Like I’m 5: TorchServe
• 🎥 How to Serve PyTorch Models with TorchServe
• How to deploy PyTorch models on Vertex AI
• Quantitative Comparison of Serving Platforms
• Efficient Serverless deployment of PyTorch models on Azure
• Deploy PyTorch models with TorchServe in Azure Machine Learning online endpoints
• Dynaboard moving beyond accuracy to holistic model evaluation in NLP
• A MLOps Tale about operationalising MLFlow and PyTorch
• Operationalize, Scale and Infuse Trust in AI Models using KFServing
• How Wadhwani AI Uses PyTorch To Empower Cotton Farmers
• TorchServe Streamlit Integration
• Dynabench aims to make AI models more robust through distributed human workers
• Announcing TorchServe

Made with contrib.rocks.

This repository is jointly operated and maintained by Amazon, Meta and a number of individual contributors listed in the CONTRIBUTORS file. For questions directed at Meta, please send an email to opensource@fb.com. For questions directed at Amazon, please send an email to torchserve@amazon.com. For all other questions, please open up an issue in this repository here.

TorchServe acknowledges the Multi Model Server (MMS) project from which it was derived