Truss uses containerized environments to ensure consistent model execution across deployments. While the default Truss image works for most cases, you may need a custom base image to meet specific package or system requirements.

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Setting a base image inconfig.yaml

Specify a custom base image in config.yaml:
config.yaml
base_image:
  image: <image_name:tag>
  python_executable_path: <path-to-python>
  • image: The Docker image to use.
  • python_executable_path: The path to the Python binary inside the container.

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Example: NVIDIA NeMo Model

Using a custom image to deploy NVIDIA NeMo TitaNet model:
config.yaml
base_image:
  image: nvcr.io/nvidia/nemo:23.03
  python_executable_path: /usr/bin/python
apply_library_patches: true
requirements:
  - PySoundFile
resources:
  accelerator: T4
  cpu: 2500m
  memory: 4512Mi
  use_gpu: true
secrets: {}
system_packages:
  - python3.8-venv

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Using Private Base Images

If your base image is private, ensure that you have configured your model to use a private registry

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Creating a custom base image

You can build a new base image using Truss’s base images as a foundation. Available images are listed on Docker Hub.

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Example: Customizing a Truss Base Image

Dockerfile
FROM baseten/truss-server-base:3.11-gpu-v0.7.16
RUN pip uninstall cython -y
RUN pip install cython==0.29.30

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Building & Pushing Your Custom Image

Ensure Docker is installed and running. Then, build, tag, and push your image: 
docker build -t my-custom-base-image:0.1 .
docker tag my-custom-base-image:0.1 your-docker-username/my-custom-base-image:0.1
docker push your-docker-username/my-custom-base-image:0.1