Awesome

❗ANNOUNCEMENT: Security Changes❗

TorchServe now enforces token authorization enabled and model API control disabled by default. These security features are intended to address the concern of unauthorized API calls and to prevent potential malicious code from being introduced to the model server. Refer the following documentation for more information: Token Authorization, Model API control

TorchServe

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

🚀 Quick start with TorchServe

# 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

🚀 Quick start with TorchServe (conda)

# 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

🐳 Quick Start with Docker

# Latest release
docker pull pytorch/torchserve

# Nightly build
docker pull pytorch/torchserve-nightly

Refer to torchserve docker for details.

🤖 Quick Start LLM Deployment

VLLM Engine

# Make sure to install torchserve with pip or conda as described above and login with `huggingface-cli login`
python -m ts.llm_launcher --model_id meta-llama/Llama-3.2-3B-Instruct --disable_token_auth

# Try it out
curl -X POST -d '{"model":"meta-llama/Llama-3.2-3B-Instruct", "prompt":"Hello, my name is", "max_tokens": 200}' --header "Content-Type: application/json" "http://localhost:8080/predictions/model/1.0/v1/completions"

TRT-LLM Engine

# Make sure to install torchserve with python venv as described above and login with `huggingface-cli login`
# pip install -U --use-deprecated=legacy-resolver -r requirements/trt_llm.txt
python -m ts.llm_launcher --model_id meta-llama/Meta-Llama-3.1-8B-Instruct --engine trt_llm --disable_token_auth

# Try it out
curl -X POST -d '{"prompt":"count from 1 to 9 in french ", "max_tokens": 100}' --header "Content-Type: application/json" "http://localhost:8080/predictions/model"

🚢 Quick Start LLM Deployment with Docker

#export token=<HUGGINGFACE_HUB_TOKEN>
docker build --pull . -f docker/Dockerfile.vllm -t ts/vllm

docker run --rm -ti --shm-size 10g --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:8080 -v data:/data ts/vllm --model_id meta-llama/Meta-Llama-3-8B-Instruct --disable_token_auth

# Try it out
curl -X POST -d '{"model":"meta-llama/Meta-Llama-3-8B-Instruct", "prompt":"Hello, my name is", "max_tokens": 200}' --header "Content-Type: application/json" "http://localhost:8080/predictions/model/1.0/v1/completions"

Refer to LLM deployment for details and other methods.

⚡ Why TorchServe

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 Meta Llama Chatbot locally on Mac
Monitoring using Grafana and Datadog

🤔 How does TorchServe work

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

🏆 Highlighted Examples

Serving Meta Llama with TorchServe
Chatbot with Meta Llama 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

🛡️ TorchServe Security Policy

SECURITY.md

🤓 Learn More

https://pytorch.org/serve

🫂 Contributing

We welcome all contributions!

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

📰 News

💖 All Contributors

Made with contrib.rocks.

⚖️ Disclaimer

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