tensorflow serving

docker run -p 8501:8501 \
  --mount type=bind,source=/path/to/my_model,target=/models/model \
  registry.ghost-prod.alphabravo.io/ghost-base/tensorflow-serving:<tag>

This command:

• Exposes port 8501 for the REST API
• Mounts your model directory to /models/model (the default model path)
• Uses the default model name "model"

Serve models via REST API

Once your container is running, you can make predictions via the REST API:

curl -d '{"instances": [1.0, 2.0, 5.0]}' \
  -X POST http://localhost:8501/v1/models/model:predict

The REST API endpoint follows the pattern: http://localhost:8501/v1/models/<model_name>:predict

Serve models via gRPC

TensorFlow Serving also exposes a gRPC API on port 8500:

docker run -p 8500:8500 \
  --mount type=bind,source=/path/to/my_model,target=/models/model \
  registry.ghost-prod.alphabravo.io/ghost-base/tensorflow-serving:<tag>

You can then connect to the gRPC endpoint at localhost:8500 using a gRPC client library in your application.

Serve a custom model name

To serve a model with a different name than the default "model", override the command arguments:

docker run -p 8501:8501 \
  --mount type=bind,source=/path/to/my_model,target=/models/my_model \
  registry.ghost-prod.alphabravo.io/ghost-base/tensorflow-serving:<tag> \
  --model_name=my_model \
  --model_base_path=/models/my_model

You can then access it at:

curl -d '{"instances": [1.0, 2.0, 5.0]}' \
  -X POST http://localhost:8501/v1/models/my_model:predict

Advanced configuration

For more configuration options, including guidance on how to serve multiple models, multiple versions of models, and configure batching, see the TensorFlow Serving documentation.

Image variants

Ghost hardened images come in different variants depending on their intended use. Image variants are identified by their tag.

• Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

  • Run as a nonroot user
  • Do not include a shell or a package manager
  • Contain only the minimal set of libraries needed to run the app

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

Migrate to a Ghost hardened image

To migrate your application to a Ghost hardened image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Ghost hardened image. This and a few other common changes are listed in the following table of migration notes.

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Ghost hardened images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshooting migration

The following are common issues that you may encounter during migration.

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Ghost hardened images is to use Docker Debug to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as the nonroot user. Ensure that necessary files and directories are accessible to the nonroot user. You may need to copy files to different directories or change permissions so your application running as the nonroot user can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Ghost hardened images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Ghost hardened images and update your Dockerfile if necessary.