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=cu102
# 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=cu102
# 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-nightly
Refer to torchserve docker for details.
- 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
- Export your model for optimized inference. Torchscript out of the box, ORT, IPEX, TensorRT, FasterTransformer
- 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 usecase with many supported out of the box
- Metrics API: out of box support for system level metrics with Prometheus exports, custom metrics and PyTorch profiler support
- Model Server for PyTorch Documentation: Full documentation
- TorchServe internals: How TorchServe was built
- Contributing guide: How to contribute to TorchServe
- 🤗 HuggingFace Transformers
- Model parallel inference
- MultiModal models with MMF combining text, audio and video
- Dual Neural Machine Translation for a complex workflow DAG
For more examples
We welcome all contributions!
To learn more about how to contribute, see the contributor guide here.
- Grokking Intel CPU PyTorch performance from first principles: 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