Recommended Installation Method#

Triton SDK Container with Docker Launch Mode#

The recommended way to install Model Analyzer is from the Triton SDK docker container available on the NVIDIA GPU Cloud Catalog.

1. Choose a release version The format is <rYY.MM> where, YY=year and MM=month

For example, the release from May of 2022 is <r22.05>

export RELEASE=<rYY:MM>

2. Pull the SDK container

docker pull nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk

3. Run the SDK container

docker run -it --gpus all \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v <path-to-output-model-repo>:<path-to-output-model-repo> \
    --net=host nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk

Replacing <path-to-output-model-repo> with the absolute path to the directory where the output model repository will be located. This ensures the Triton SDK container has access to the model config variants that Model Analyzer creates.

Important: You must ensure the <path-to-output-model-repo> is identical on both sides of the mount (or else Tritonserver cannot load the model)

4. Run Model Analyzer with Docker Launch Mode Be sure to use --triton-launch-mode=docker, when running Model Analyzer.

Alternative Installation Methods#

Specific Version with Local Launch Mode
Main with Local Launch Mode
Using Pip
From the Source

Specific Version with Local Launch Mode#

This method allows you build a specific version of Model Analyzer’s docker. This installation method uses --triton-launch-mode=local by default, as all the dependencies will be available.

1. Clone Model Analyzer’s Git Repository

git clone https://github.com/triton-inference-server/model_analyzer.git -b <rYY.MM>

Replacing <rYY.MM> with the version of Model Analyzer you want to install. For example, r22.05 is the release for May of 2022

2. Build the Docker Image

cd ./model_analyzer

docker build --pull -t model-analyzer .

Model Analyzer’s Dockerfile bases the container on the corresponding tritonserver (from step 1) containers from NGC.

3. Run the Container

docker run -it --rm --gpus all \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v <path-to-triton-model-repo>:<path-to-triton-model-repo> \
    -v <path-to-output-model-repo>:<path-to-output-model-repo> \
    --net=host model-analyzer

Replacing <path-to-triton-model-repo> with the absolute path to the directory where the Triton model repository will be located. Replacing <path-to-output-model-repo> with the absolute path to the directory where the output model repository will be located.

Main Branch with Local Launch Mode#

This method allows you build a docker container using the main branch of Model Analyzer. This installation method uses --triton-launch-mode=local by default, as all the dependencies will be available.

1. Build Triton SDK Container from Main Follow the instructions found at Build SDK Image

2. Build Model Analyzer’s Main Docker Image

git clone https://github.com/triton-inference-server/model_analyzer.git -b main

cd ./model_analyzer

docker build -t model-analyzer --build-arg TRITONSDK_BASE_IMAGE=<built-sdk-image> .

Replacing <built-sdk-image> with the absolute path to the directory where the Triton SDK image is located

3. Run the Container

docker run -it --rm --gpus all \
    -v /var/run/docker.sock:/var/run/docker.sock \
    -v <path-to-triton-model-repo>:<path-to-triton-model-repo> \
    -v <path-to-output-model-repo>:<path-to-output-model-repo> \
    --net=host model-analyzer

Replacing <path-to-triton-model-repo> with the absolute path to the directory where the Triton model repository will be located. Replacing <path-to-output-model-repo> with the absolute path to the directory where the output model repository will be located.

Pip#

You can install pip using:

sudo apt-get update && sudo apt-get install python3-pip

Model analyzer can be installed with:

pip3 install triton-model-analyzer

If you encounter any errors installing dependencies like numba, make sure that you have the latest version of pip using:

pip3 install --upgrade pip

You can then try installing model analyzer again.

Optionally, if you want Model Analyzer to generate PDF reports instead of HTML, you should also run the following:

sudo apt-get update && sudo apt-get install wkhtmltopdf

From the Source#

To build model analyzer from the source. You’ll need to install the same dependencies (tritonclient) mentioned in Pip Install.
After that, you can use the following commands:

git clone https://github.com/triton-inference-server/model_analyzer
cd model_analyzer
./build_wheel.sh <path to perf_analyzer> true

In the final command above we are building the triton-model-analyzer wheel. You will need to provide the build_wheel.sh script with two arguments. The first is the path to the perf_analyzer binary that you would like Model Analyzer to use. The second is whether you want this wheel to be linux specific. Currently, this argument must be set to true as perf analyzer is supported only on linux. This will create a wheel file in the wheels directory named triton-model-analyzer-<version>-py3-none-manylinux1_x86_64.whl. We can now install this with:

pip3 install wheels/triton-model-analyzer-*.whl

After these steps, model-analyzer executable should be available in $PATH.

Notes:

Triton Model Analyzer supports all the GPUs supported by the DCGM library. See DCGM Supported GPUs for more information.