Triton Command Line Interface (Triton CLI)

Note

Triton CLI is currently in BETA. Its features and functionality are likely to change as we collect feedback. We're excited to hear any thoughts you have (especially if you find the tool useful) and what features you'd like to see!

Triton CLI is an open source command line interface that enables users to create, deploy, and profile models served by the Triton Inference Server.

Table of Contents

| Pre-requisites | Installation | Quickstart | Serving LLM Models | Serving a vLLM Model | Serving a TRT-LLM Model | Additional Dependencies for Custom Environments | Known Limitations |

Pre-requisites

When using Triton and related tools on your host (outside of a Triton container image), there are a number of additional dependencies that may be required for various workflows. Most system dependency issues can be resolved by installing and running the CLI from within the latest corresponding tritonserver container image, which should have all necessary system dependencies installed.

For vLLM and TRT-LLM, you can use their respective images:

• nvcr.io/nvidia/tritonserver:{YY.MM}-vllm-python-py3
• nvcr.io/nvidia/tritonserver:{YY.MM}-trtllm-python-py3

If you decide to run the CLI on the host or in a custom image, please see this list of additional dependencies you may need to install.

Installation

Currently, Triton CLI can only be installed from source, with plans to host a pip wheel soon. When installing Triton CLI, please be aware of the versioning matrix below:

Triton CLI Version	TRT-LLM Version	Triton Container Tag
0.0.7	v0.9.0	24.04
0.0.6	v0.8.0	24.02, 24.03
0.0.5	v0.7.1	24.01

Install from GitHub

Install latest from main branch:

pip install git+https://github.com/triton-inference-server/triton_cli.git

It is also possible to install from a specific branch name, a commit hash or a tag name. For example to install triton_cli with tag 0.0.6:

GIT_REF="0.0.7"
pip install git+https://github.com/triton-inference-server/triton_cli.git@${GIT_REF}

Install from Source

# Clone repo for development/contribution
git clone https://github.com/triton-inference-server/triton_cli.git
cd triton_cli

# Should be pointing at directory containing pyproject.toml
pip install .

Quickstart

The instructions below outline the process of deploying a simple gpt2 model using Triton's vLLM backend. If you are not in an environment where the tritonserver executable is present, Triton CLI will automatically generate and run a custom image capable of serving the model. This behavior is subject to change.

Note

triton start is a blocking command and will stream server logs to the current shell. To interact with the running server, you will need to start a separate shell and docker exec into the running container if using one.

# Explore the commands
triton -h

# Add a vLLM model to the model repository, downloaded from HuggingFace
triton import -m gpt2

# Start server pointing at the default model repository
triton start --image nvcr.io/nvidia/tritonserver:24.04-vllm-python-py3

# Infer with CLI
triton infer -m gpt2 --prompt "machine learning is"

# Infer with curl using the generate endpoint
curl -X POST localhost:8000/v2/models/gpt2/generate -d '{"text_input": "machine learning is", "max_tokens": 128}'

Serving LLM Models

Triton CLI simplifies the workflow to deploy and interact with LLM models. The steps below illustrate how to serve a vLLM or TRT-LLM model from scratch in minutes.

Note

Mounting the huggingface cache into the docker containers is optional, but will allow saving and re-using downloaded huggingface models across different runs and containers.

ex: docker run -v ${HOME}/.cache/huggingface:/root/.cache/huggingface ...

Also, usage of certain restricted models like Llama models requires authentication in Huggingface through either huggingface-cli login or setting the HF_TOKEN environment variable.

Serving a vLLM Model

vLLM models will be downloaded at runtime when starting the server if not found locally in the HuggingFace cache. No offline engine building step is required, but you can pre-download the model in advance to avoid downloading at server startup time.

The following models have currently been tested for vLLM through the CLI:

• gpt2
• opt125m
• mistral-7b
• falcon-7b
• llama-2-7b
• llama-2-7b-chat
• llama-3-8b
• llama-3-8b-instruct

Example

docker run -ti \
  --gpus all \
  --network=host \
  --shm-size=1g --ulimit memlock=-1 \
  -v ${HOME}/models:/root/models \
  -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
  nvcr.io/nvidia/tritonserver:24.04-vllm-python-py3

# Install the Triton CLI
GIT_REF="0.0.7"
pip install git+https://github.com/triton-inference-server/triton_cli.git@${GIT_REF}

# Authenticate with huggingface for restricted models like Llama-2 and Llama-3
huggingface-cli login

# Generate a Triton model repository containing a vLLM model config
triton remove -m all
triton import -m llama-3-8b-instruct --backend vllm

# Start Triton pointing at the default model repository
triton start

# Interact with model
triton infer -m llama-3-8b-instruct --prompt "machine learning is"

# Profile model with GenAI-Perf
triton profile -m llama-3-8b-instruct --backend vllm

Serving a TRT-LLM Model

Note

By default, TRT-LLM engines are generated in /tmp/engines/{model_name}, such as /tmp/engines/gpt2. They are intentionally kept outside of the model repository to improve re-usability across examples and repositories. This default location is subject to change, but can be customized with the ENGINE_DEST_PATH environment variable.

The model configurations generated by the CLI prioritize accessibility over performance. As such, the default number of model instances for each model will be set to 1. This value can be manually tuned post-generation by modifying the instance_group field in each model's corresponding config.pbtxt file. Increasing the instance counts may result in improved performance, especially for large batch sizes. For more information, please see here.

The following models are currently supported for automating TRT-LLM engine builds through the CLI:

• gpt2
• opt125m
• llama-2-7b
• llama-2-7b-chat
• llama-3-8b
• llama-3-8b-instruct

Note

Building a TRT-LLM engine for Llama-2-7B or Llama-3-8B models may require system RAM of at least 48GB of RAM.

Example

# NOTE: Mounting /tmp is optional, but will allow the saving and re-use of
# TRT-LLM engines across different containers. This assumes the value of
# `ENGINE_DEST_PATH` has not been modified.

# This container comes with all of the dependencies for building TRT-LLM engines
# and serving the engine with Triton Inference Server.
docker run -ti \
  --gpus all \
  --network=host \
  --shm-size=1g --ulimit memlock=-1 \
  -v /tmp:/tmp \
  -v ${HOME}/models:/root/models \
  -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \
  nvcr.io/nvidia/tritonserver:24.04-trtllm-python-py3

# Install the Triton CLI
GIT_REF="0.0.7"
pip install git+https://github.com/triton-inference-server/triton_cli.git@${GIT_REF}

# Authenticate with huggingface for restricted models like Llama-2 and Llama-3
huggingface-cli login

# Build TRT LLM engine and generate a Triton model repository pointing at it
triton remove -m all
triton import -m llama-3-8b-instruct --backend tensorrtllm

# Start Triton pointing at the default model repository
triton start

# Interact with model
triton infer -m llama-3-8b-instruct --prompt "machine learning is"

# Profile model with GenAI-Perf
triton profile -m llama-3-8b-instruct --backend tensorrtllm

Additional Dependencies for Custom Environments

When using Triton CLI outside of official Triton NGC containers, you may encounter the following issues, indicating additional dependencies need to be installed.

1. If you encounter an error related to libb64.so from triton profile or perf_analyzer such as:

perf_analyzer: error while loading shared libraries: libb64.so.0d

Then you likely need to install this system dependency:

apt install libb64-dev

2. If you encounter an error related to libcudart.so from triton profile or perf_analyzer such as:

perf_analyzer: error while loading shared libraries: libcudart.so

Then you likely need to install the CUDA toolkit or set your LD_LIBRARY_PATH correctly. Refer to: https://developer.nvidia.com/cuda-downloads.

3. To build TensorRT LLM engines, you will need MPI installed in your environment. MPI should be shipped in any relevant Triton or TRT-LLM containers, but if building engines on host you can install them like so:

sudo apt install libopenmpi-dev

Known Limitations

• Models and configurations generated by Triton CLI are focused on ease-of-use, and may not be as optimized as possible for your system or use case.
• Triton CLI currently uses the TRT-LLM dependencies installed in its environment to build TRT-LLM engines, so you must take care to match the build-time and run-time versions of TRT-LLM.
• Triton CLI currently does not support launching the server as a background process.