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# Latest release
docker pull pytorch/torchserve
# Nightly build
docker pull pytorch/torchserve-nightlyRefer 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
- SOTA GenAI performance using
- 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
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