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AI Infrastructure & Serving

Transformer Deploy

Transformer Deploy is an open-source Python project that converts Hugging Face transformer models into optimized files and configuration for inference, and supports serving them with NVIDIA Triton on CPU or GPU.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Transformer Deploy — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you want a self-managed conversion-and-serving workflow for Hugging Face transformer inference, rather than only running a model in a Python application.

Who it is for

  • ML and platform engineers deploying Hugging Face transformer inference in their own environment.
  • Teams serving classification, named-entity recognition, dense-embedding or text-generation workloads.

Documented uses

  • Convert an eligible transformer model into optimized ONNX Runtime or TensorRT deployment files.
  • Generate Triton configuration and a model-repository layout.
  • Run inference through NVIDIA Triton and query its HTTP service.
Before you choose

Requirements & limits

The repository is public, open source and licensed under Apache-2.0.

  • Model eligibility depends on exportability to ONNX.
  • The documented GPU workflow requires NVIDIA drivers and NVIDIA Container Toolkit.
  • The conversion tool and the NVIDIA Triton server are separate stages in the example workflow.
  • The README's performance comparisons are reported examples and should not be treated as guarantees for a different model or machine.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Convert a Hugging Face model for Triton

Generate optimized model files and Triton configuration for a model that can be exported to ONNX.

Before you start

  • Docker
  • A local clone of the Transformer Deploy repository, including its demo folder
  • For the documented GPU command: NVIDIA drivers and NVIDIA Container Toolkit

Steps

  1. Clone the repository and change into its directory.

  2. Pull the documented Transformer Deploy Docker image.

  3. Run the image with the project directory mounted, using convert_model with the model name, backend choices and sequence lengths.

Expected outcome

The conversion command generates optimized model files, Triton configuration and a Triton model-repository layout.

Guide limitations

  • The documented example uses GPU execution; GPU use requires NVIDIA drivers and NVIDIA Container Toolkit.
  • The project supports models that can be exported to ONNX; the example does not establish results for every model or hardware setup.
Documentation 1 ↗
Source-based practical guide

Start Triton with a generated model repository

Launch NVIDIA Triton with model files generated by Transformer Deploy.

Before you start

  • Docker
  • A populated local triton_models directory
  • The documented NVIDIA Triton server image

Steps

  1. Mount triton_models at /models in the Triton container.

  2. Run the container with the documented ports and shared-memory setting, installing transformers at startup.

  3. Start Triton with /models as its model repository.

Expected outcome

Triton starts its documented HTTP, gRPC and metrics services for the mounted model repository.

Guide limitations

  • The example installs transformers at runtime; the project recommends pre-installing it in a custom Docker image for production.
  • The documented command alone does not demonstrate client-side inference querying.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Transformer DeployConvert an eligible transformer model into optimized ONNX Runtime or TensorRT deployment files.ServerThe repository is public, open source and licensed under Apache-2.0.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
ELS-RD/transformer-deploy · Official homepage title · retained in its source language
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