All NVIDIA Merlin components are available as open source projects. However, a more convenient way to make use of these components is by using our Merlin NGC containers. We have created Docker containers for NVIDIA Merlin that are hosted on NGC.
Containers allow you to package your software application, libraries, dependencies, and runtime compilers in a self-contained environment. These containers can be pulled and launched right out of the box. You can clone and adjust these containers if necessary.
The following table provides a list of Dockerfiles that you can use to build the corresponding Docker container:
| Container Name | Dockerfile | Container Location | Functionality |
|---|---|---|---|
merlin-hugectr |
dockerfile.ctr |
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-hugectr | NVTabular and HugeCTR |
merlin-tensorflow |
dockerfile.tf |
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-tensorflow | NVTabular, TensorFlow, and HugeCTR Tensorflow Embedding plugin |
merlin-pytorch |
dockerfile.torch |
https://catalog.ngc.nvidia.com/orgs/nvidia/teams/merlin/containers/merlin-pytorch | NVTabular and PyTorch |
Building our containers is a two-step process. We first build the Merlin BASE_IMAGE using the dockerfile.merlin file. This container depends on two upstream containers: nvcr.io/nvidia/tritonserver and nvcr.io/nvidia/tensorflow, from which it pulls the necessary dependencies for Triton Inference Server and RAPIDS tools. It also builds and installs other Merlin requirements, such as scikit-learn, XGBoost, etc.
We then build framework-specific containers based off of the BASE_IMAGE. See the table above for which Dockerfile corresponds to which framework-specific container.
The base image is not made available publicly, but the framework-specific containers based on it are. The two-stage build process takes roughly 1 hour. Running all of the tests for all Merlin libraries can take a couple of additional hours, depending on which framework you're building.
We tag this image as nvcr.io/nvstaging/merlin/merlin-base:${MERLIN_VERSION} and it is used to create the framework-specific containers. There are ARGs in the Dockerfile to define which version of the containers to use. You can override the defaults when building the image like below.
docker build -t nvcr.io/mycompany/merlin-base:${MERLIN_VERSION} --build-arg TRITON_VERSION=23.04 - < docker/dockerfile.merlinIn this example we are tagging the base image as nvcr.io/mycompany/merlin-base:${MERLIN_VERSION}. The tag Merlin uses when building this image in our own build pipeline is nvcr.io/nvstaging/merlin/merlin-base:${MERLIN_VERSION}.
We also provide Dockerfiles for creating framework-specific containers: dockerfile.tf, dockerfile.torch, and dockerfile.ctr. These are all based on the BASE_IMAGE created in the previous step and install the associciated deep learning frameworks.
To build the PyTorch container, we specify the BASE_IMAGE build arg to use the base image that we just created.
docker build -t ngcr.io/mycompany/merlin-torch:${MERLIN_VERSION} --build-arg BASE_IMAGE=nvcr.io/mycompany/merlin-base:${MERLIN_VERSION} - < docker/dockerfile.torchEach of the Dockerfiles have many ARGs defined, most of which have defaults set. Sometimes the defaults fall out of date, because the Merlin team overrides them in our build process as demonstrated above. To see the ARG values used in each of our released containers, see the Merlin Support Matrix